Linguistic Society of America Spanish Stress Assignment within the Analogical Modeling of Language Author(s): David Eddington Source: Language, Vol. 76, No. 1 (Mar., 2000), pp. 92-109 Published by: Linguistic Society of America Stable URL: http://www.jstor.org/stable/417394 Accessed: 01/10/2010 13:21 Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://links.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at http://links.jstor.org/action/showPublisher?publisherCode=lsa. 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Linguistic Society of America is collaborating with JSTOR to digitize, preserve and extend access to Language. http://links.jstor.org SPANISH STRESS ASSIGNMENTWITHINTHE ASSIGNMENTWITHINTHE ANALOGICALMODELING ANALOGICALMODELING OF LANGUAGE DAVID EDDINGTON Mississippi M ss ss pp State Un University vers ty The adven advent oof non nonlinear nearphono resulted edin n an exp ud es re phonology ogy has resu explosion os on oof sstudies Spanish sh relating a ngtoo Span ruc ureand and sstress ress p but mos most oof these hese sstudies ud es cclaim a m too represen syllable sy ab e sstructure placement, acemen bu ngu s c representlinguistic not ac actual ua mechan mechanisms smsused used by speakersin n speechproduc ruc ureno competence compe enceand language anguagesstructure, on speech production and comprehens comprehension. on The pre within h n Skou Skousen's en MODELING NGOF OF LANGUAGE (AML) ANALOGICAL ANALOG CALMODEL present en study udy is couched w AML reflect ec how speakersde determine erm nelinguistic behaviors orssuch such 1992, 1995 (1989, 1989 1992 1995). AML aattempts emp stoo re ngu s c behav as sstress ress p unfamiliar am arwordneeds word needs too be sstressed, AML, when an un ressed speakers placement. acemen Accord According ng too AML access their he rmen mental a lexicon, or wordss words similar m artoo the he word in n ques hen app he sstress ress ex con searchfor question, on then applyythe oof the he word n ques ound too the he word in word(s) s found question. on The 44,970 most common Span he da database abasefor or the he sstudy. 970 mos Spanish sh words served as the udy AML correc correctlyy ress too abou about 94% oof these hese words words. The errors it made cclosely reflect ec the he pa assigned ass gned sstress ose y re pattern ernoof errors made by Span errorsmade n a sstudy children dren in Aske'ss Moreover, Aske Spanish-speaking sh speak ngch 1988 Moreover udy by Hochberg (1988). nonce word probe (1990) ha na native nat ve speakers are sens sensitive ve too a cer certain a n subpa n 1990 showed that subpattern ernin ress ass which ch does no not rece receive ve represen n ru rulee mode models. s Spanish Span sh sstress assignment-a gnmen a subpa subpattern ernwh representation a onin The ana model oof Span ress m mirrors rrorsAske Aske'ss findings.* analogical og ca mode Spanish sh sstress nd ngs * INTRODUCTION. INTRODUCTION Within W th nthe the generat studies eson on Span tradition, t on stud generative vetrad Spanish shstress ass assignment gnment have been numerous since s nce the of advent adventof nonlinear non nearand and numerous,espec especially a y autosegmentalphoautosegmenta Harriss1983 Terrell 1976 1986, Harr 1983, 1989 1989, 1995 1995, Hooper & Terre (e.g. g Den Os & Kager 1986 nology no ogy (e 1976, Roca Saltarelli Sa tare and 1997, 1997 1988, 1988 1990, 1990 1991, 1997 1991 1997, 1997, 1997 Lipski L psk 1976). The Whitley Wh t ey 1976) studies es iss to prov concise se representat goal of these stud goa provide de a conc representation onof the linguistic ngu st c structures n Span involved nvo ved in stress Studies Stud essuchas such as these a m to be re relevant evant Spanish sh placement. p acement usuallyy cclaim usua to competence-the tac tacitt know that allows ows themto them to commun communi-knowledge edge that speakershave thata cate. In th cate thiss regard regard,K Kiparsky parskystates: In phono he sys rules es and under orms m he speaker phonology, ogy the system em oof ru underlying y ng forms might gh be a represen representation a onoof the speaker'ss oof the KNOWLEDGE he sys n the he language; not in n any sense a mechan mechanism sm systematic ema cre relationships a onsh psamong among words in anguage no which wh ch iss app whenever words are spoken and heard heard. (1975:198; so Chomsky & Ha Hallee 1968 1968: applied ed wheneverwords 1975 198 see aalso 117, Brad 117 1980:38) 38 Bradley ey 1980 In other words and derivations of phono vat onsof words, the forma are not formalisms, sms ru rules, es andder phonological og ca ana analyses yses arenot to mirror m rror mechanisms. mechan sms usuallyy thought usua psychological psycho og ca characterized zedby common that are cons considered dered Spanish Span sh stress iss character commonlyyoccurr occurring ngpatterns patternsthatare with th numerousexcept Several proposa regular, regu ar aalong ong w exceptions ons to these patterns patterns.Severa proposalsson how to accountfor account for the genera Farrell forth, and as Farre generalizations zat onsand except exceptions ons have been put forth on studies stud es the structureof structureof Span notes, (1990:37) notes Spanish sh stress ass assignment gnmenthave bas basically ca y taken one of two approaches: The genera summarized zedas as follows. o ows E Either hercer certain a n pa generative veapproach approachcan be summar patterns ernsare genera generated edor they hey are not. IIf the areno he bas basicc parame n too set in oo res restrictive r c veaa manner mechanisms smsmus must parameters ersare se manner,a var variety e yoof ad hoc mechan be prov ow for or marg he bas basicc parame set in n such a way as too aallow provided ded too aallow ow marginal na pa patterns. erns IIf the parameters ersare se too oo much freedom, mechanisms smsmus must be prov restrict r c the reedom a var he genera variety e y oof mechan provided ded too res generation onoof marg marginal na patterns. pa erns * I express my ssincerest nceres thanks hanks too Roya Steve eve Chand Harald d Baayen Skousen, S well as too the he Chandler, er Hara Royal Skousen Baayen, as we or their he r input with h this h s sstudy. erees for ndeb ed too Jose Ramon anonymous referees, anonymousre npu and he help pw addition, on I am indebted udy In add Alameda A amedafor or grac he compu version onoof h hiss frequency Without hou graciously ous yaallowing ow ng me access too the computerized er zedvers requencyd dictionary. c onary W he presen would d have been impossible. it, the present sstudy udy wou mposs b e 92 SPANISH STRESS ASSIGNMENTWITHIN AML 93 relate ate to psycho of these ana formalisms smsof If the forma mechanisms, sms then analyses yses do not re psychological og ca mechan most correctiss not germaneto a psycho ch ana the debateaboutwh debateaboutwhich psychological og ca theory analysis ys siss mostcorrect about how stress ass abouthow assignment gnmentmay may take p place. ace differs ffers qu analyses yses of linguistic ngu st ccompetence previous ousana competence gn f cant yfrom prev quite tessignificantly My study d couched within th n Skousen Skousen'ss ANALOGICAL relates atesto to Span as itt re placement.1 acement 1It iss couchedw Spanish sh stress p model that attemptsto OFLANGUAGE OFLANGUAGE MODELING 1992, 1995) (1989, 1992 1995). AML iss a mode (AML) (1989 such as stress p behaviors orssuch determine nelinguistic reflect ref ect how speakersdeterm placement. acement It iss not ngu st c behav se. Rather model of language a comp Rather,itt iss a comprehension onand product production onper per se anguage comprehens complete ete mode behavior. or Accord model of how memory tokens may be used to pred mode predict ct linguistic ngu st c behav According ng theirrmenta to stressan stress an unknownword mental arises sesto to AML word, speakersaccess the AML, when the need ar n quest to the word in m arto words that are ssimilar searchfor wordsthatare lexicon ex con and searchfor question. on They then app applyy n quest thiss regard found to the word in the stressof stress of the word(s) foundto question. on In th regard,AML has much Medin n & Schaffer modelss (Aha et aal. 1991 with th other exemp in n common w 1991, Med exemplar-based ar-basedmode overview ew of exemp Shanks 1995 for an overv & Schank1989; Schank 1989; see Shanks1995 Riesbeck esbeck& models, s 1978, R 1978 exemplar armode et aal. 1994 for a compar Daelemans emanset and Dae ) comparison sonof AML and Aha et aal.). n th n thiss study ana Iw will show that for the databasein analogy ogy correct correctlyyass assigns gns stress in n Span threshout ssignificant ablee to threshout nstancesand and iss ab about 94% of the instances about94% gn f cantsubpatterns subpatternsin Spanish sh rules es or schemas schemas.22One without thout resort One of these subpatternswas stress p resorting ngto ru placement acementw n a studyby study by Aske (1990) (1990), though itt p native ve speakersin for nat plays ays shown to be ssignificant gn f cantfor Nevertheless, ess th thiss pattern stress. Neverthe Spanish shstress accountsof Span e-basedaccountsof n any currentru currentrule-based no partin analogy ogy iss found in na dence for ana Furtherevidence analogy. ogy Furtherev accounted for by ana iss successfu successfullyy accountedfor errorsmade by the ana of the errorsmade comparison sonof analogical og ca errors.A compar placement acementerrors study of stress p (Hochberg 1988) demonstrates children dren(Hochberg Spanish-speaking sh-speak ngch model and those made by Span mode anguageuse use. actual language with th actua consistent stentw that ana produces outcomes cons analogy ogy producesoutcomes analogy ogy has been used too Traditionally, Trad ona y ana OFLANGUAGE. OFLANGUAGE 11. ANALOGICAL MODELING general ru rule, e a not obey a genera outcome come does no account for accoun or excep outcomes. comes When an ou exceptional ona ou sought; that ha he excep exceptional ona one iss sough m ar too the phonetically ca y ssimilar form orm that ha iss seman semantically ca y or phone ha it does no n such a way that orm in not exceptional ona form he excep form orm iss then n uence the hen sa said d too influence thiss sort of rules. es What makes th general ru of the genera application cat onof according ng to the app develop deve op accord nab tyof of ru rules esto to der patchup the inability derive ve serves to patchup ultimately t mate yservesto suspicious c ousiss thatitt u analogy ana ogysusp analogs ogs or orms can serve as ana or wha what forms set eeither her for aall forms. m s are se orms In add addition, on no limits analogy ogy to be invoked. nvoked orderfor ana n orderfor on how ssimilar m artwo two forms must be in regular aras as we assumes thatall regu well of ana analogy, ogy AML assumesthata notion onof traditional t onanot In contrastto contrastto the trad forms. (The nf uence of other forms analogical og ca influence to the ana as irregular attributed butedto forms may be attr rregu arforms analogical og ca mode of the ana model detailssof specific f c deta readeriss referredto referredto Skousen 1989 and 1992 for spec presentarticle.) ce) beyond the scope of the presentart discussion scuss on iss beyondthe thiss d and the aalgorithm andthe employs; oys;th gor thmitt emp reasonitt iss rem reminiscent n scent For thissreason sm Forth same mechanism. In AML aall formsareattr InAML butedto the samemechan forms are attributed characterization zat onof overall character extracts an overa model extractsan neither thermode of connect example, e ne connectionism. on sm For examp schemata. the data in n the form of ru rules es or schemata modelss on stmode and connectionist between AML andconnect differences fferencesbetween gn f cantd There are, however Thereare however, ssignificant onlyy one ou predict c on outnetworks works pred Connectionist on s ne 1995). Connec 1989, 1995 1995, Skousen 1989 (Chandler Chand er 1995 or moreoutcomes thatone ormoreoutcomes tythatone the probability ctstheprobab come fora for a ggiven AML predicts whilee AMLpred context,wh ven context and feedbackfrom feedback from training n ngand extensive ve tra reextens networksrequire will be chosen w chosen. Connect Connectionist on stnetworksrequ teacher. In teacher or external externa of training tra n ngor sort any entail a 'teacher', whilee AML does not enta teacher wh n ercon network work oof interconn a ne activation va on in patterns erns oof ac ored as pa n orma on iss sstored connectionism, connec on sm information 1 1994). Durieux eux 1994 Daelemans, emans and Dur G s Dae well (Gillis, Dutch ch as we n Du placement acemen in AML has been app ress p applied ed too sstress (Daelemans Dae emans model exemplar-based exemp ar basedmode using ng an n Du Dutch ch us Similar S m arresu placement acemen in or sstress ress p resultss were found ound for eet aal. 1994 1993). Gilliss eet a al. 1994 1994, G Gilliss eet aal. 1993 1994, G 2 94 LANGUAGE, VOLUME 76, NUMBER 1 (2000) nformat on words. In AML nd v dua words nected nodes; there iss no representat AML, the information representation onof individual mentallexicon. contentsof the menta ex con n a databaseof databaseof exemp contained nedin iss conta exemplars arsrepresent representing ngthe contentsof networkscannot me In contrast connectionist on stnetworkscannot addedto at any ttime. Thiss databasemay be addedto Th contrast,connect retrained nedto to include the new nc udethe thout hav data without completely ete y retra having ng to be comp readilyy accept new dataw read data. data arru rulee mode model. Ru Rulee more familiar useful to compareitt to the morefam To understandAML itt iss usefu rules. es AML derive ve surfaceforms surface forms from under modelss der mode application cat onof ru underlying y ngforms by the app and a mechan mechanism smfor for search databaseof fu uses a databaseof words,anda searching ngand compar fullyy spec comparing ng specified f ed words n quest m arto to the word in words most ssimilar of the wordsmost The behavior orof words.33Thebehav those words question ongenera generallyy small chance words has a sma ess ssimilar m arwords of less behavior orof behavior, or aalthough thoughthe behav predicts pred ctsthe behav n the same m arwordsthatbehave words that behave in nf uence of groups of ssimilar well. The influence of app applying y ng as we terature(e MacWhinnn the psycho well attestedin manneriss we (e.g. g Stemberger& MacWh psycholinguistic ngu st cliterature for effects a and andAML AML (Skousen 1988), 1988) measuring measur nggang gang algorithm a gor thm specific spec f c provides prov des ney 1989:67-71). 1989:67-71) and rulee accounts between AML andru accounts. differences fferencesbetween should d cclarify A concrete examp ar fy the d examplee shou with w th front before suffixes suff xes some is s retained reta nedbefore stem-final stem-f na In Span /k/ k vowels, vowe s beginning beg nn ng Spanish, sh > suffixes xes + dim'. d m Other Othersuff -ito to diminutive: m nut ve:/pok+ o/ o such as the d 'few, few /pokito/ pok to appear pok+ CostaR Rican'. can kostarr Oense'Costa ca + ense > /kostarriOense/ Costa Rica fricative: cat ve:CostaR to cause /k/ k to become a fr rulee to the effect thatk that k > accountfor th thiss by postu A ru rule-based e-basedapproachcan accountfor postulating at nga ru o/ -] that app n wh n the stratain which ch n wh which ch -ense iss aff n the stratain affixed, xed but not in applies es in words are storedas who xedwordsarestoredas wholes es and unaffixed affixed xedandunaff n contrast contrast,aall aff AML, in --ito to iss aff affixed. xed In AML arises sesto to determ ex con When the need ar determine ne mental lexicon. to the menta in n a databasecorrespond ngto databasecorresponding the attributes conductedbased on theattr butes ex con iss conductedbasedon searchof the lexicon word, a searchof the phoneticcshapeof thephonet shapeof a word basicc aalgorithm gor thmiss the fo context). The bas ven context) following: ow ng: n quest e the ggiven question on ((i.e. of the word in contextual ex ua space looking ook ng n con outward wardin henmove move ou ha con context ex and then examples esoof that or ac actual ua examp rs searchfor We first systematically ema ca yeeliminate context ex we sys he ggiven ven con m na e rom the away from outward wardaway working ng ou for or nearby examp examples. es In work 1995:217) 217 Skousen 1995 supracontexts. ex s (Skousen called ed supracon contexts ex s ca general con more genera creating ngmore hus crea variables, var ab es thus ven context iss dependent for the ggiven analog ogfor that a word iss chosen as an ana The probab probability tythat 1995:217). (Skousen 1995:217) on threeder three derived vedpropert properties es(Skousen greaterthe context, the greaterthe ven context the examp examplee iss to the ggiven m arthe the more ssimilar (1) prox proximity: m ty:the model;; analogical og ca mode selected ected as the ana being ng se that examplee be chances of thatexamp having ng the same other examples es hav examplee iss surroundedby otherexamp (2) gang effect: iff the examp examples esiss substanbehaving ngexamp m ar ybehav these ssimilarly of se selecting ect ngthese then the probability tyof behavior, behav or thentheprobab ncreased; ttially a y increased; model iff there analogical og ca mode selected ected as the ana an examp examplee cannot be se heterogeneity: ty:an (3) heterogene ven context context. oser to the ggiven behavior, or ccloser with th d different fferentbehav are more ssimilar examples, es w m arexamp can const consti-what examp examples escan constrain nwhat nce they constra mportantssince are important properties esare These der derived vedpropert the factors se ythefactors These areprecisely analogs. ogs Theseareprec ngana betweencompeting tuteanalogs, tuteana debetweencompet ogs andtheydec andthey decide ack to ana analogy ogy lack. appealssto that traditional thattrad t ona appea consti-n homogenoussupracontexts homogenous supracontextsconst nedin words contained AML, aall of the wordsconta According Accord ngto to AML modelss analogical og ca mode that can serve as ana words from thiss set thatcan tute the ana set. It iss the wordsfromth analogical og ca set will have nf uence that a word or gang of words w for a ggiven amountof influence context. The amountof ven context context will ven contextw thatthe ggiven probability tythatthe terms of the probab n termsof on the ggiven ven context iss expressedin of retention onof above, retent ven above examplee ggiven another.In the examp group or another behavior orof of one groupor adopt the behav adoptthe another. by /0/ 0 iss another ts rep replacement acementby stem-final stem-f na /k/ k iss one behav behavior, or and its 3In h s s udy he phonem c a r bu eso wordsare assumed o be he re evan var ab es AML however can a so ncorpora eo hervar ab es such as soc o ngu s c var ab es Skousen 1989 97 100 SPANISH STRESS ASSIGNMENTWITHINAML 95 The probab will be ass behavior orof of anotherword anotherword probability tythat a ggiven ven context w assigned gned the behav iss based on the degree of ssimilarity between the context and the word. Each word m ar ty ggiven ven memberof a group of words w memberof with th ssimilar m archaracter characteristics st csmay aalso so affect the behav behavior or of the ggiven context.44 However context However, the members of the group affect the ggiven ven context ven collective ect ve behav behavior or iss extracted individually. nd v dua y No gglobal oba representat representation onof the group group'ss co from the data behavior or may resu resulttthat that appearsru rulee- or schema-based schema-based. data, aalthough thoughbehav Once the ana there are two ways in n wh which ch its ts contentscan contents can determined, ned thereare analogical og ca set iss determ influence nf uencethe the behav behavior orof of the ggiven rst iss thata that a word 1989:82). The ffirst ven context (Skousen 1989:82) could cou d be random selected ected from among those in n the ana behavior or set, and the behav randomlyyse analogical og ca set of that word app context. The other poss would d be to applied ed to that of the ggiven ven context possibility b ty wou determine determ newh which ch behav behavior oriss most frequentamong n the set set, and ass frequentamong the words in assign gn that behavior behav orto to the ggiven context. In dea with th probab data,peop ven context dealing ng w probabilistic st cdata peoplee appearto take attermethod method iss (Messick ck & So advantageof both of these methods (Mess 1957). The latter Solley ey 1957) n the currentstudy assumed in study. thatfor any ggiven context, Returning Return ngto the examp examplee from Span Spanish, sh AML can pred predict ctthatfor ven context will be reta retained nedbefore 0 before others before certa certain n suff suffixes xes and rep others. Th Thiss /k/ k w replaced aced by /0/ n the words of the database that, in database,/kl prediction pred ct on iss based on the ssimple mp e fact that k appears 0 before others before some suff suffixes xes and /0/ others. Thus that exists sts among Thus, the genera generalization zat onthatex the words of the database iss app context. If we were interested nterestedin n applied ed to the ggiven ven context would d happento the Ik oco 'crazy' n its ts d diminutive m nut veform knowing know ng what wou Ikl of loco crazy in form, and the from the set of homogenoussupracontexts then the behav behavior or analog ana og chosen fromthe homogenous supracontextswere poco poco, thenthe of poco > po would dbe be extendedana nsteadof of lobIGito obIG o polklito k to wou analogically og ca yto producelo/k/ito, o k to instead from rom loco. oco The proposa that stored exemp determine nelanguage use may proposalthatstored exemplars arsof past exper experience encedeterm anguageuse counterintuitive ntu t veto many characterization zat onof linguistic n appearcounter many. Sure Surely, y a gglobal oba character ngu st c data in the formof form of a ru would d be morep more plausible constraints nts rule, e schema schema, or prototypewou aus b eggiven ven the constra on memory evidence dence that behav behavior or may be based on stored memory. Neverthe Nevertheless, ess there iss ev Hintzman ntzman1986 Hintzman ntzman& & Lud Ludlam am1980 Medin n& exemplars exemp ars(Chand (Chandler er1995 1995, H 1986, 1988 1988, H 1980, Med & Schaffer 1978 1978, Nosofsky 1988) 1988). In add addition, t on perform performing ngrap rapid d searches of memory for storedexemp unfeasible. b e Rob Robinson nson (1995) demonstrateshow indexing n exemplars arsiss not unfeas ndex ng in the form of databaseinversion nvers on may p rolee in n such searches searches. play ay a ro currentmodelssof of humancogn assumethatthe brain nprocesses nformat on Manycurrentmode cognition t onassumethatthebra processesinformation in n a mass Welsh sh 1978 massively ve ypara parallel e manner(Mars (Marslen-Wilson en-W son& We 1978, Se Seidenberg denberg& & McC McCleleland and 1989 Kirchner rchner1999fora 1999 for a d discussion scuss onof of how exemp 1989, Stemberger1985 Stemberger1985, 1994;see K exemplars ars ntosuch such mode ex con as env envisioned s onedby may figure mayf gure into models). s) A lexicon by Bybee (1985 (1985, 1988) 1988), in n wh which ch m aritems tems are interconnected, would d great phonetically phonet ca yand semant semantically ca yssimilar nterconnectedwou greatlyyenhance enhance nteract veact activation vat onmode searching search ngand process processing ng speed speed. In an interactive model, hear hearing, ng see seeing ng or the word fat activates vates hundredsof hundredsof d different fferent words or saying say ng fat, for examp example, e part partially a yact words:wordsthatbeg that have threephonemes,or thatarere partsof words:wordsthat begin nw withf, thf or thathavethreephonemes that are related ated to obes on. In otherwords other words, aall of the attr obesity, ty or that rhymew rhyme withfat, thfat and so on attributes butesof of a activate vate aall the words in n the lexicon ex con that have an attr ggiven ven context part partially a y act attribute butein n common. It iss not necessary to inspect common each and andevery n the lexicon, nspect every word in ex con on onlyy those that have been most h thathave activated vatedas as a resu resulttof of the theirrssimilarity to the ggiven highly gh y act m ar tyto ven context context. 4 Prasadaand Prasadaand P Pinker nker (1993) evidence dence that ha gang eeffects ec s d 1993 prov provide de ev disappear sappearwhere where type ype frequency requencyiss h high, gh as in n regu ense forms. orms In their he r nonce word sstudy, regular arEng English sh pas past tense udy no gang eeffects ec s were found ound for or regu regular ar items. ems The connec connectionist on s ssimulation mu a onoof the he same items, ems though, hough erroneous erroneouslyydemons demonstrated ra edgang gang eeffects. ec s In contrast con ras too the heconnec connectionist on s ou comescons consistent s en w with h the outcome, come AML producesou producesoutcomes henoncewords nonce wordstudy udy(EddingEdd ng ton on 2000 2000). LANGUAGE, VOLUME 76, NUMBER 1 (2000) 96 nteract veact could d activation, vat on ana analogical og ca sets cou By means of such para parallel e process processing ng and interactive be constructed constructedandeva and evaluated uatedat at the by comprehens comprehension onand speed required speedrequ redby theoretically theoret ca y production. product on IN SPANISH.Stress INSPANISHS ress may fall a on any oof the he last as three hree sy 22. STRESSPLACEMENT syllables ab es word. In genera of a Span vowel-final -f na words iss the norm (e general, penu penultt stress on vowe (e.g. g Spanish sh word while wh e words with w th final f na stress are cons ttiene ene 's/he s he has consonant-final consonant-f na considered deredregu has'), ) regular ar mantel 'table tab eccloth'). creduloo oth ) Antepenu waysregarded regardedas irregular (e.g. (e g mante Antepenulttstressiss aalways rregu ar(e (e.g. g credu nce itt runscounterto runs counterto the ffirst rsttwo two more genera tendencies. es Preantepenu 'gullible') gu b e ) ssince general tendenc Preantepenultt stress iss rare certain nverba followed owed by two cclitic verbal forms are fo tc rare, and occurs on onlyy when certa him/her'). m her ) g guardadndose pronouns(e.g. pronouns(e 'saving ng them for h guardadndoselos ossav The genera vowel ffinal na words are norma stressed, and consogeneralization zat onthat vowe normallyypenu penulttstressed nant-final nant-f na words are norma na stressediss comp somewhatwhen word-f word-final na normallyyffinal complicated catedsomewhatwhen -s iss cons considered. dered Hooper and Terre Terrell (1976) observe that in n nonverba nonverbal morpho morphology, ogy when -s funct functions ons as the p na The same marker,stress iss norma plural ura marker penultt not ffinal. normallyypenu n verba aalso so ho holds ds true in verbal morpho nd catessecond second persons morphology ogywhen -s indicates person singular. ngu ar WORD END ENDING NG FINAL F NAL STRESS PENULT STRESS PENULTSTRESS ANTEPENULTSTRESS ANTEPENULTSTRESS Vowel Vowe 178 2494 178 Consonant Consonan 798 1085 96 20 909 94 Isl Is Consonant(except Consonan 778 176 2 Is!) excep Is TABLE11. S n mos Stress ress p most frequent words. placement acemen in requen Span Spanish shwords The fact that penu n words end n -s iss illustrated ustratedin n Tab Tablee penultt stress iss the norm in ending ng in 11. The datacome data come fromthe from the 44,829 n the A words in Alameda amedaand and 829 most frequentpo frequentpolysyllabic ysy ab cwords Cuetos frequency d vowel-final -f na dictionary ct onary(1995) (1995). That penu penultt stress iss the norm for vowe words iss cclearly consonant-final na words are aalmost most as likely ear y demonstratedbut demonstrated,but consonant-f ke y to be stressed on the penu stressedon na sy until ffinal na -s words penultt as on the ffinal syllable. ab e That is, s of course course, unt are removed nce they patternmore cclosely with th vowe vowel-final -f na words words. In short removed, ssince ose y w short,penu penultt stress iss vviewed ewed as the norm for words end n -s or a vowe whilee ffinal na stress iss ending ng in vowel, wh considered cons dered regu n aall consonantsexcept consonantsexcept ss. regular arfor words end ending ng in It iss important stress iss contrast contrastive: ve:sabdna sabdna 'savannah', mportantto note that Span Spanish shstress savannah sdbana 'sheet'. sheet Th Thiss iss espec n verba evident dent in verbalforms:encontrdra forms: encontrdra's/he s he found especially a y ev found, imp. mp subj subj.', encontrard 's/he s he w will ffind'; s he sought nd ; busco 'II search search', busco 's/he sought'. It iss for th thiss reason that many stud studies esof of Span stress consider derthe the effects of morpho as we Spanish shstresscons morphology ogyas well as those of phono studies eseven even suggest thatverba that verbal andnonverba and nonverbalstress ass phonology. ogy Some stud assignment gnment are governedby different fferentru rules es (e whilee othersstr othersstrive veto to ach governedby d (e.g. g Roca 1988) 1988), wh achieve eveaa un unified f ed Harriss 1989) will returnto returnto th thiss issue n ?4 ssue in analysis ana ys s (e (e.g. g Harr 1989). I w ?4. 33. THE DATABASE DATABASE. 33.1. 1 ITEMSINCLUDED INTHEDATABASE INTHE To test DATABASE. es sstress ress p placement acemen w within h n AML AML, it was construct a databaseof database of Span necessary to constructa Spanish sh words that wou would d serve as the rough mental lexicon. ex con Of course equivalent equ va ent of a Span Spanish shspeaker speaker'ssmenta course, the quest question onof of whether words have individual regular regu arpo polymorphemic ymorphem cwords nd v dua representat representation onn in the menta mental lexicon ex con iss a hot ssue P Pinker nkerandh andhiss co hotlyy debatedissue. colleagues eagueshaveadducedev have adducedevidence dencethatthesewords thatthese words have no individual nd v dua entr derived ved on online ne (Jaegeret entries, es but are der (Jaeger et aal. 1996 1996, P Pinker nker 1991 1991, Pinker P nker& & Pr Prince nce 1994 Prasada& P Pinker nker 1993) 1994, Prasada& 1993). If th thiss iss the case case, such words cou could d not exert ana nf uence as AML wou analogical og ca influence would d requ require. re Otherev evidence, dence however however, suggeststhata suggests that all, or at least east the most frequentmorpho frequentmorphologiog words are storedas who wholes es (A Gordon1999, Baayenet callyy comp ca complex ex wordsarestoredas (Alegre egre & Gordon1999 Baayenet aal. 1997a 1997a, SPANISH STRESS ASSIGNMENTWITHINAML 97 Maneliss & Tharp 1977 Butterworth1983 1983, Bybee 1995 1977, Sereno & Jongman 1997) 1995, Mane 1997). Pinker nkerandPr theirrbets bets somewhatand somewhatand acknow Even P and Prince ncehave have hedged the thiss poss acknowledged edgedth possi-Chandler er(1993) Chandler erand and Skousen (1997) Furthermore,Chand (1994:331). Furthermore (1993), Chand (1997), and bility b ty (1994:331) demonstratedthat the data ccited ted in n supportof Seidenberg Se denberg and Hoeffner (1998) have demonstratedthat Pinker's P nker smode ruleless e ess mode model of language model as we well. reinterpreted nterpretedto supporta ru anguagemay may be re reconcilee the apparent evidence dence iss to assume a Perhapsthe best way to reconc apparentlyyconf conflicting ct ng ev n wh lexicon ex con in which ch at least east the most frequent frequentlyyoccurr occurring ngmorpho morphologically og ca ycomp complex ex words have individual nd v dua representat n such a way that the theirr organized zed in representation, on but are stored or organ Feldman dman & Fow Fowler er 1985, 1988 1988, Fe relationships at onsh psare transparent(Bybee 1985 morphological morpho og ca re n favor of mass massive ve storage evidence dence in 1987, Katz et aal. 1991) 1987 course, most of the ev 1991). Of course of morpho with th ssimple morphologically og ca ycomp complex ex words comes from languages anguages w moderatelyy mp e to moderate Futurestudies esw will needto the roleeof need to focus on thero of storage complex comp ex morpho systems. Futurestud morphological og ca systems in nh Turkish. sh highly gh y agg agglutinating ut nat nglanguages anguages such as Turk Anotherissue ssue to be reso resolved ved iss how large ex ca databaseneeds databaseneeds to be assumedin n arge a lexical an ana n parton the goa one'ss analogical og ca ana analysis. ys s The answerdependsin goal of the ana analysis. ys s If one aaim m iss to correct behavior orof of the largest numberof instances, nstances correctlyypred predict ct the linguistic ngu st c behav argest numberof databases are more eff efficient. c ent For examp Gilliss et aal. 1992 on larger arger databasesare example, e the work by G Dutch stress ass nd cates that more correct pred ze assignment gnment indicates predictions ct onsare made as the ssize of the databaseincreases. ncreases And Baayenandh (Baayenet aal. 1997b 1997b,Bertram colleagues eagues(Baayenet Baayen andhiss co et aal. 1999 Schreuder& Baayen 1997) foundthatone found that one wou would d have 1999, Schreuder& 1999, de Jong et aal. 1999 to cons consider deraa databaselarge nc udeeven even the least eastfrequent words frequentlyyoccurr occurring ngwords argeenoughto enoughto include in n order to account for subject sua y reaction on ttimes mes to vvisually ratings ngs and react subjective ve frequency rat words. But extens extensive ve databasesare databasesare not requ n an ana required redin analysis ys s depresentedsimplex presenteds mp ex words model language acquisition s t onphenomena phenomena,error nstance language anguageacqu anguageusage usage. For instance, ssigned gned to mode and historical h stor ca shifts sh fts may be mode modeled ed us onlyy databasesconsisting st ng of on using ng databasescons prediction, pred ct on several hundredinstances severa nstances (Derw 1989). 1994, Skousen 1989) (Derwing ng & Skousen 1994 I opted for a med medium-sized um-s zeddatabase because of the process processing ngrestr restrictions ct ons database,part partlyybecause of the computerprogramused which ch aallowed owedon aboutfive ve thousandinstances.5 The nstances 5The onlyy aboutf computerprogramused, wh n theA the Alameda amedaandCuetosfrequencyd andCuetosfrequencydictionary ct onarywerechosen were chosen 44,970 970 most frequentwordsin as the database database.Th Thiss includes nc udes words w with th a frequencyof more. The million on or more frequencyof 66.66 per m databaseconsisted stedof of base forms and verb of base forms forms, andverb forms, inflectional nf ect ona var variants antsof resulting resu t ngdatabasecons t c pronouncomb nat ons plus p us cclitic pronouncombinations. The most frequentwords were chosen nce exper ghthat highchosen, ssince experimentation mentat onhas shown thath than low-frequency Allen en et ow-frequencywords words (e (e.g. g A rapidly d ythan frequency words are accessed more rap aal. 1992 ess subjectto 1982). error(e.g. g MacKay 1982) subject to error(e 1977), and are less 1992, Scarboroughet aal. 1977) Thiss suggests thatfrequentformsaremoreread Th ke y and therefore,more likely available, ab e andtherefore frequentforms are more readilyyava to be se selected ected as ana analogs. ogs 33.2. 2 VARIABLES INCLUDED INTHEDATABASE INTHE DATABASE. variables ab es The T he nex selecting ec ng the he var next issue ssue was se to use in n encod words. Skousen(1989) Skousen (1989) andDerw and Derwing ng andSkousen(1994) and Skousen (1994) encoding ng the 44,970 970 words note thatvar thatvariable ab ese selection ect on iss one of the majorcha with thAML AML. Skousensuggests Skousen suggests majorchallenges engesw some gu Wheneverpossible, variables ab esshou should d be used b e enough var (1989:51-53). Wheneverposs guidelines de nes (1989:51-53) so that each instance nstance iss d distinct st nct from every other variables ab es other. One shou should d aalso so use the var cclosest osest to the var variable ab ewhose whose behav behavior oriss be phonemicc encoding ng the phonem being ng pred predicted. cted By encod content and sy contentand structureof the ffinal na threesy arge y three syllables guidelines de nes were largely ab es these gu syllable ab e structureof 5I am mos most gra GertDur Durieux euxoof the he Un version onoof or aallowing ow ng me too use h hiss vers University vers yoof An Antwerp werpfor grateful e u too Ger Skousen'ss AML programtoo under Skousen undertake akethis h s sstudy. Hiss vers and version ongrea he numbero numberof var variables ab esand udy H greatlyy increases ncreases the n the instances ns ances in he da database abasethat ha may be used used. 98 LANGUAGE, VOLUME 76, NUMBER 1 (2000) entries esconta contained nedpreantepenu followed. fo owed S Since nce none of the entr tress itt was not necessary preantepenulttsstress, than the ffinal na three sy to encode more thanthe syllables. ab es n anAML matterof predeterm an AML ana The process of se variables ab esin predeterminnanalysis ys siss not a matterof selecting ect ng var hand. It iss in which ch var variables ab esare are most important n fact des desirable rab eto to ing ng wh mportantto the task at hand rre evantat at the outset outset. For examp variables ab esthat that may seem irrelevant include nc ude many var example, e the most will precede a n determ whetherthe indefinite ndef n teart article c eaa or an w variable ab ein determining n ngwhetherthe important mportantvar whetherthe word beg with th a vowe vowel or consonant noun or adject consonant. begins ns w ggiven ven Eng English sh nounor adjective veiss whetherthe will be aalways If th thiss iss the on n the ana correctarticle c ew chosen. variable ab ein ways be chosen onlyy var analysis, ys s the correctart are included-the rre evantvar variables ab esare nc uded-the phonem However, iff other seem However seemingly ng y irrelevant phonemiccmake up of the noun fo article-AML c e-AML beg article, c e and the word preced begins ns to following ow ng the art preceding ngthe art towarda (Skousen 1989) thaterrorsaalways s itt correct 1989). Thatis, eakagetowarda correctlyypred ways predict pred ctleakage predicts ctsthaterrors np involve nvo ve the use of a in ce versa (e chair). r) place ace of an (e apple), e) not vvice (e.g. g a app (e.g. g an cha The need to include nc ude var variables ab esthat that may seem un furtherevidenced denced in n unimportant mportantiss furtherev Skousen'ss ssimulation Skousen mu at onw with th a groupof F Finnish nn shpast tense forms forms.Formostof For most of these verbs verbs, the cho choice ce of the past tense morphemeappears na two morphemeappearsto be dependenton what the ffinal vowel of the verb stem iss aa. However to are, or iff the vowe However, sorta- 'to phonemes of the stem are case. It does not become sors sorsi as a ru rule-based e-based oppress' appearsto be an except oppress exceptional ona case would d pred sorti. Neverthe nstead itt becomes sort Nevertheless, ess AML correct analysis ana ys s wou predict; ct;instead, correctlyypred predicts cts thiss outcome th but the is s made madeon on n the stem the basis bas s of the which ch sortao in outcome, prediction pred ct on stem, wh has in n common w with th a group of other verba verbal stems which ch has a past tense stems, each of wh form end n -t -ti. A stemstem-internal nternaoo may be an irrelevant rre evantvar for the major of variable ab efor ending ng in majority tyof these verbs but not for sortasorta-. Th Thiss wou would dnothavebecomeev not have become evident dentiff on variables ab es verbs,butnot onlyy the var that appearedmost re relevant evantwere were included n the ana nc uded in Thiss suggests that speakers analysis. ys s Th do not makea make a gglobal determination nat onof wh which ch var variables ab esarere are relevant n advance evantin rules es oba determ advance,as ru variables ab estakepart take partin n the ana and the cruc crucial a var variables ab es imply. mp y Instead Instead,aall var analogical og ca search search,andthe can on determined nedindirectly afterthe ana constructedand inspected. onlyy be determ nd rect yafterthe analogical og ca set iss constructedand nspected to the issue ssue of var variable ab ese selection ect on in n Span could d arguethatthe Returning Return ngto Spanish, sh one cou arguethatthe most relevant re evantvar variable ab efor for stressass stress assignment word's ffinal na phoneme or whetherthe penultt gnmentiss a word phoneme,orwhetherthepenu osed or open n the ffinal na threesy three syllables syllable sy ab e iss cclosed open. Neverthe Nevertheless, ess aall of the phonemesin ab es were included. nc uded G Given ven the contrast contrastive venatureof natureof stress n verba verbalforms stress,espec especially a y in forms, itt was aalso so necessaryto nc ude some var variables ab esthatcou that could dd between phonem necessary to include distinguish st ngu shbetween phonemically ca y forms. Therefore variables ab esindicating the personandthe equivalent equ va entforms Therefore,var nd cat ngthe personand the tense formof form of each verb were included. nc uded These var variables ab esaalso so served to d verbs from nonverbs distinguish st ngu shverbs nonverbs. entries esin n the A Alameda amedaandCuetosd and Cuetos dictionary are not taggedfor Unfortunately, Unfortunate y the entr ct onaryarenot tagged for part of speech and I was ob verbal or nonverba speech,66 andI obliged ged to ass assign gn the words verba nonverbalstatusby statusby hand hand. In the major of cases verbal statusof statusof the entr majority tyof cases, the verba entries eswas was read readilyyapparentIn apparent.In those few cases wherea where a wordcou word could dbe be eeither theraa verbora verb or a nonverb nonverb,(e (e.g. g encuentro'encounter', encounter or 'II ffind'), nd ) I ass assigned gned itt what seemed to me to be the most common use of the word word. For encuentrothe encuentrothe mean nd seemed to be the most common use of the word meaning ng 'II ffind' word. In four cases did d not seem to be more common than another cases, one mean meaning ng d another,and the assignment ass gnmentwas made random randomly. y Allowing A ow ng category-amb category-ambiguous guouswords such as encuentrointo nto the databasecou databasecould d be vviewed ewed as prob that neither problematic. emat c It may be thatne therencuentroas encuentro as a verb nor encuentroas encuentroas a nonverb iss frequentenough tse f ((i.e. frequent enough by itself e 66.66 words per m million on or above) to mer meritt inclusion nc us on in n the database database.Yet Yet, because of the theirr comb combined nedfrequency frequency, such words are 6 The frequency Juilland andandChang and Chang-Rodriguez requencyd dictionary c onaryby by Ju Rodr guez1964 (1964) iss tagged agged for or par partoof speech speech, bu but does not include no nc ude frequency n orma onon on aall oof the he tokens okens that requencyinformation ha appearedin n their he rda database. abase In con contrast, ras A Alameda ameda and Cue Cuetos os (1995) s the he frequencies okens 1995 list requenc esoof aall tokens. SPANISH STRESS ASSIGNMENTWITHIN AML 99 Thiss means n essence that the databasemay nonverb.Th as a verb or a nonverb included nc udedeeither theras essence, thatthe means, in tems w with th a frequencybe below ow 66.66 words per m contain conta n severa several items million. on nc us on of a few lower-frequency n the databaseiss not In one respect ower-frequencywords in respect, the inclusion naall poss databasecannotcontain a cr critical t ca prob Since nce the databasecannotconta words, itt was Spanish shwords possible b e Span problem. em S n some pr ze of th artificial f c a menta mentallexicon ex con in m t the ssize thiss art Thiss way. Th principled nc p edway necessaryto limit to the task at hand tems are irrelevant rre evantto in n no way implies ower frequency items hand, on mp es that lower onlyy factor. In rea that frequency was chosen as the limiting with th m t ng factor reality, ty the on onlyy prob problem em w theirr arb thernonnonarbitrary traryass assignment gnmentas eeither category-ambiguous guouswords iss the including nc ud ng these category-amb encuentro ro as a verb verbs or verbs verbs. However thirteen rteenvar variables ab esused used to encode encuen However, of the th that indicate nd catethe nine ne var variables ab esthat the phonem content of and as a nonverb(see be below), ow) the n phonemicc contentof dent ca in n both forms forms. In other words word'ss phono each sy words, the word syllable ab e are identical phonological og ca ts verba nonverbalstatus structureiss frequentenough verbal or nonverba status. nc us on but not its frequentenough for inclusion, In addition Inadd the above mentioned so exper with thvar t onto to theabovement onedvar various ouscomb combi-variables, ab es I aalso experimented mentedw n ffinding of othermorpho variables. ab es I was part nterestedin nations nat onsof particularly cu ar yinterested nd ng a way morphological og ca var vowel-final -f na preter na stress results ts were to aallow ow vowe stress. The best resu assigned gned ffinal preterittforms to be ass when verba variables ab esindicating obtained obta nedwhen verbal forms included nc udedthree three var nd cat ngthe tense form of the nstead of one one. Repeat variable ab emore more than once iss the on onlyy way to we weight ght Repeating ngaa var verb, instead verb one var variable ab e heav heavier er than another another. Th Thiss implies mp es that the tense form of the verb iss considered cons deredthree three ttimes mes more important that any ssingle coda. onset, nuc nucleus eus or coda ng e onset mportantthat Thiss sortof Th sort of var variable ab ewe somewhatunorthodoxfor admittedly, tted y ad hoc and somewhatunorthodoxfor weighting ght ng is, s adm an AML ana anAML but nevertheless, When the members redoutcome outcome.Whenthemembers producesthe desired ess itt producesthedes analysis, ys s butneverthe of the databasewere databasewere removedone removed one at a ttime me and AML AML'ss aalgorithm used to searchfor search for gor thmused from the rema n the database tems in preteritt errorratefor polysyllabic ysy ab cpreter database,the errorrateforpo remaining n ngitems analogs ana ogs fromthe forms was 32%(of 156) iff the tensevar formswas tense variable ab ewas was included The ratedecreased onlyy once once. Theratedecreased nc udedon to 15%when 15% when the var variable ab ewas was included nc udedthree three ttimes, nc ud ng itt more than three mes but including ttimes mes d did d not resu n any furtherdecrease resulttin furtherdecreasein n the errorrate essence, 27 fewer errors errorrate. In essence occur on preter with th ffinal na stress when th whilee none of weighted, ghted wh thiss var variable ab eiss we preterittverbs w the rest of the items n the databaseare tems in databaseare affected affected. For th eft these dup reason, I left thiss reason duplicate cate n the ana variables var ab es in additional t ona 27 errors an add nv ted to incorporate ncorporatean analysis. ys s The readeriss invited into nto the ensu her taste. thiss we hiss or hertaste of var variables ab esiss too ad hoc for h weighting ght ngof analysis ys s iff th ensuing ngana In sum Tablee 2) 2). (see Tab consists sts of 13 var variables ab es(see encoding ng of each word cons sum, the encod Variables Var ab es WORD STRESS 13 12 11 2 1 4 3 6 5 8 7 10 9 0 Final F na a 1 n s o e r p personal persona hablaron hab aron Penult Penu 6 o n r a a bl b pt p pt p pt p Note: No e 6 indicates nd ca esthird h rdpersonp not app apply. y variable ab edoes does no nd ca espre ha a var ense - indicates nd ca esthat preterit er tense. ura p person plural; pt indicates Variables: Var ab es 11. The coda oof the he word word'ss final na sy here iss one one. syllable, ab e if there 22. The nuc nucleus eusoof the he word word'ss final na sy syllable. ab e 33. The onse onset oof the he word word'ss final na sy here iss one one. syllable, ab e if there 44. The coda oof the he penu here iss one one. penult sy syllable, ab e if there 55. The nuc nucleus eus oof the he word word'ss penu monosyllabic. ab c he word iss monosy penult sy syllable, ab e or 0 if the 66. The onse onset oof the he penu here iss one one. penult sy syllable, ab e if there 77. The coda oof the he an here iss one one. syllable, ab e if there antepenult epenu sy 88. The nuc nucleus eus oof the he an monosyllabic. ab c bisyllabic sy ab c or monosy he word iss b syllable, ab e or 0 if the antepenult epenu sy 99. The onse onset oof the he an here iss one one. syllable, ab e if there antepenult epenu sy 10. Tense 10 he item em iss no not a verb verb. Tense, or 0 if the 11. Tense 11 he item em iss a verb verb. Tense, if the 12. Tense 12 he item em iss a verb verb. Tense, if the 13. The person the 13 he verb iss con verb. he item em iss a verb or if the conjugated uga edfor, TABLE22. Var Variables ab esused used too encode words in database. abase n da LANGUAGE, VOLUME 76, NUMBER 1 (2000) 100 44. ANALOGICAL CONSISTENCY. CONSISTENCY As noted noted, AML assumes that aall known words are stored in n the menta mental lexicon ex con w theirr inherent with th the nherentstress stress. Therefore Therefore,iff AML iss asked to that the correctstress correct stress w will be ass word, the probab assign ass gn stress to a known word probability tythatthe assigned gned iss 100 percent novel or memory cond conditions t ons are imperfect, percent. But iff the word iss nove mperfect stress determined erm ned on the basiss oof the he bas he ne he word in n ques placement p acemen iss de neighbors ghbors oof the question. on nvo ves the extentto extent to wh which ch ssimilarly words have Analogical Ana og ca cons consistency stency involves m ar ybehav behaving ng wordshave ssimilar m ar charac characteristics. er s cs For examp most words that ha are finally ressed are aalso so example, e if mos na y sstressed m ar there iss a h consissmorphologically morpho og ca yand phonem phonemically ca yssimilar, high gh degree of ana analogical og ca cons Where Wherethere there is s a of word can tency. tency high h gh degree cons consistency, stency the stress p placement acementof a wordcan determined nedon on the bas basiss of the stress p ts ne usuallyy be determ usua placement acementof its s on neighbors, ghbors that is, the bas basiss of other items tems that sharecharacter share characteristics st csw with th the word in n quest question. on In order to determ determine nethe the ana tenfold d crossanalogical og ca cons consistency stency of Span Spanish sh stress p placement, acement a tenfo validation va dat onwas was performed This Th s consisted cons stedof of the database databaseof of words into nto 44,970 970 performed. dividing d v d ng ten sect sections ons of 497 words each each. The membersof membersof each group were then treatedas treatedas the test es items, whilee the he members oof the he rema nine ne groups compr he training ems wh set remaining n ng n comprised sed the ra n ng se from rom wh which ch ana were chosen. chosen analogs ogs Given G venthefactthatthedatabaseconta the fact thatthe databasecontained nedsevera severalinflectional nf ect ona var variants antsof of manywords manywords, a poss confound ound ex exists. s s IIf the he ggiven context ex iss the he ad n ec ona possible b e con ven con adjective ec ve ro rojas, as itss inflectional variants var antsrojo rojo, roja will be included nc uded in analogical og ca set and influence n the ana nf uence itt to roja, and rojos w receive rece ve penu penultt stress stress. The idea dea beh behind nd determ determining n ngthe the ana analogical og ca cons consistency stency of the databaseiss to see how ana analogy ogyrespondsto respondsto an unknownword unknownword. If rojo rojo, roja roja, and rojos are aallowed owed too serve as poss possible b e ana analogs ogs for or ro rojas, as the he sys system em iss no not ac actually ua y treating rea ng it as a comp completely e e y nove novel item. em A ssimple mp e way oof con controlling ro ng for or the he eeffect ec oof items ems that ha share the same root was to aalphabetize phabet zethe the databasepr database prior or to part partitioning t on ngitt for the tenfold en o d sstudy. udy In this h s way way, inflectional n ec ona var variants an s were grouped together oge her in n the he same test es set, and were unab set unableeto to serve as ana analogs ogs for each other other. STRESS ASS ASSIGNED GNED BY AML WORD ACTUAL STRESS domindnte dom ndn e penult penu final na podrdn plastico p as co antepenult an epenu preguntoo pregun final na penult penu pesddo debil deb penult penu TABLE33. Probab Probabilityyoofsstress ress FINAL F NAL PENULT ANTEPENULT .000 000 11.000 000 .000 000 .992 992 .007 007 .000 000 .000 000 .006 006 .993 993 .674 674 .326 326 .000 000 .000 000 11.000 000 .000 000 .673 673 .327 327 .000 000 placement p acemen accord according ngtoo AML AML. Once the databasewas Oncethe databasewas part partitioned, t onedthe the stressp stressplacement acementof of eachwordwas each word was determ determined ned according accord ngto to AML gor thm Tab AML'ss aalgorithm. Tablee 3 conta contains nsaa samp sampling ngof of outcomes computedby computedby AML. The outcomefor AML outcome for a ggiven ven word iss expressedas expressedas the probab probability tythatthe that the wordw word will be ass assigned gned stress on a certa certain n sy syllable. ab e As can be seen seen, deb weak iss incorrectly debil 'weak' ncorrect y assigned ass gnedffinal na stress stress. The preter preterittverbpregunto verbpregunto 's/he s he asked asked' iss correct correctlyyass assigned gnedffinal na stress, but aalso stress so shows the influence nf uence of hav having ng severa several ne neighbors ghborsw with th penu penultt stress stress. Under these cond conditions, t ons the success rates on the 10 groupsranged groups ranged from 92 92.2% 2% to total, 94 94.4% 4% of the 44,970 970 words tested were correct 96.8%. 96 8% In tota correctlyystressed stressed, indicating nd cat ngaa very h high ghdegreeof degree of cons consistency. stency Penu Penulttstresswas stresswas mostcons most consistent stentw with th 98 of penu 98.9% 9%of penultt stressed words correct correctlyy ass assigned gned stress stress. Word-f Word-final na stress fo followed owed cclosely ose y at 93 93.6, 6 whilee on wh onlyy 40 antepenulttstressedwordswere stressed words were most heav heavilyy influenced 40.11% % of antepenu nf uencedby by other words that aalso so have antepenu antepenulttstress stress. Anotherpossible Anotherposs b e object objection onto to the studyiss thatitt cons considers derson onlyy the h highest ghest frequency lexical ex ca items. tems In genera general, the major majority tyof of the irregularly rregu ar ybehav behaving ngwords words in n a language anguage SPANISH STRESS ASSIGNMENT WITHIN AML 101 ess ana ones. In other words consissare aalso so among the most frequentones words, there iss less analogical og ca cons would dnot not be the opt tems and and, arguab arguably, y they wou optimal ma group amonghigh-frequency tency amongh gh-frequencyitems, n pred to use to ach achieve eve the h highest ghest degree of accuracyin predicting ct ngstress ass assignment. gnment Thiss propertyof h Th evident dent when the databaseiss used high-frequency gh-frequencywords becomes ev to pred tems Four hundredn ninety-seven nety-seven ow-frequency items. predict ct the stress of a group of low-frequency n the A and Cuetos d items tems w with th a frequencyof one (0 Alameda amedaandCuetos million) on) in (0.22 per m dictionary ct onary n the initial n t a tenfo were tested aga weretested the ten tra sets used in tenfold dcross-va cross-validation dat onstudy study. against nsttheten training n ngsetsused with th an average of 91 91.8%. 8% The resu 91.1% 1% to 92 92.6%, 6% w resulting t ng success rates ranged from 91 tems found when test Thiss resu Th resulttfa fallss sslightly below ow the averagefoundwhen high gh frequencyitems ght y be testing ng the h n the numberof numberof items tems correct aalone one (94 reduction onin (94.4%). 4%) The reduct correctlyystressediss probab probablyydue n the h to the large numberof irregular tems in sets. arge numberof rregu aritems high-frequency gh-frequencytra training n ngsets I concede that there are fewer irregularities ess frequentlexical ex ca items tems rregu ar t esamong among the less stressof a larger numberof items temscou could dbe be correct andthatitt iss h andthat thatthe stressof argernumberof correctlyy possible b e thatthe highly gh yposs nsteadof of h tems tems instead training n ngset of low-frequency ow-frequencyitems, assigned ass gnedggiven ven a tra high-frequency gh-frequencyitems. would d be ignored Nevertheless, Neverthe ess ssignificant gn f cant facts about language anguage usage wou gnored iff such a step were taken taken. H tems shou should dbe be included nc uded ssince nce they p rolee rregu aritems High-frequency gh-frequencyirregular play ay a ro in n linguistic ngu s c cogn cognition. on Consider Cons der the Eng tense, the major English sh past tense majority ty of whose irregular rregu arforms are h high gh It may be thecase the case thatbetterpred thatbetterpredictions would dbe be madeaboutthephono madeaboutthe phonologiog ct onswou frequency.Itmaybe frequency cal shape of the past tense form iff on ca ower frequencyitems tems were ana on, but analogized og zedon onlyy lower would d be m missed. ssed A common erroramong erroramong ch example, e iss children, dren for examp ssignificant gn f cant facts wou the use of brang instead nsteadof of broughtas Thiss errorcomes bring. ng Th errorcomes about broughtas the past tense of br as a resu of the influence resulttof nf uenceof of certa certain nh forms such as sang sang. The high-frequency gh-frequencyirregular rregu arforms historical h stor ca move from st so due to the ana highghanalogical og ca pressureof pressureof h stinged nged to stung iss aalso verbs such as stunk stunk.It iss datasuchas datasuch as these thatlead conclude ude ead me to conc frequencyirregular rregu arverbssuchas that restr databaseto the most frequentitems tems iss the most pr way to principled nc p edway restricting ct ngthe databaseto ts ssize n orderto limit m t its ze in orderto carryout mu at ons(see (see aalso so ?3 ?3.1). 1) analogical og ca ssimulations carry out ana 44.1. 1 VERBALVERSUS NONVERBAL N ONVERBAL anaSTRESS or de determining erm n ng ana PLACEMENT. P LACEMENT One reason for nvo ves the idea dea thatstressmaybe thatstressmay be determ for verbs determined nedd differently fferent yforverbs consistency stency involves logical og ca cons and nonverbs (e Thiss op Harriss universal versa of course (e (e.g. g Harr opinion n on iss not un 1988). Th (e.g. g Roca 1988) the mattermore mattermore cclosely. ose y If theoretical ca interest nterestto to investigate nvest gatethe Therefore,itt iss of theoret 1989). Therefore 1989) verbalandnonverba verba and nonverbalstressass stress assignment thatwould d suggestthat suggest that processedseparately, y thatwou gnmentiss processedseparate verbs have ma verbal ne whilee nonverbs must be influenced nf uenced ma mainly n y by mainly n y verba neighbors, ghbors wh nonverbs. If th nonverbs thiss iss true one shou should d be greater consistency stency of verbs aalone true, the ana analogical og ca cons thanthe cons thanthe of verbsandnonverbscomb verbsand nonverbscombined. same vein, n thecons the consistency stency ned In the sameve consistency stencyof of nonverbs when considered cons dered separate consistency stency of should d be greaterthan greater than the cons separately, y shou nonverbs, verbs and nonverbscomb nonverbscombined. ned To test th thiss not notion on of cons divided v ded into nto two parts:one parts: one consistency, stency the databasewas database was d the theotherconta other phaThe words wordswereaga were again naalphacontaining n ngon only y nonverbs. nonverbs verbs, verbs containing conta n ngon onlyy betized bet zed anda and a tenfo tenfold dcross-va cross-validation dat onwas was performedTheprocedureenta edrandom randomlyy performed.The procedureentailed tems from each new group so that the groups wou evenlyy would d be even eeliminating m nat ng seven items divisible d v s b e by ten ten. Tab Tablee44 shows thatass thatassigning verbs basiss of ssimilar m arverbs gn ngverba verbalstresson stress on the bas ENTIRE ENT REDATABASE DATABASE VERBS NONVERBS VERBS ALONE NONVERBS ALONE NONVERBSALONE # OFERRORS OFERRORS 42 235 45 228 % OFERRORS OFERRORS 33.00 66.66 66.44 33.22 TABLE44. Errorra Errorrates es ana entire reda nonverbs alone. one database, abase verbs or nonverbsa analogizing og z ngen 102 LANGUAGE, VOLUME 76, NUMBER 1 (2000) the verba verbalerrorrate( errorrate (i.e. ncreasedthe e the percentageof incorrectly ncorrect ystressedwords) sslightly ght y increased nonverbsdecreasedfrom 6.6% but the errorratefor errorratefor nonverbsdecreasedfrom6 6%to to 66.4% 4%under from 33.0% to 33.2%, 0%to under 2% butthe the same cond conditions. t ons own cclass, theirrown nf uence on membersof the nonverbsare allowed owed to influence If verbsand verbs and nonverbsarea ass the onlyy membersof From an ana tt e Froman errorsvaries es very little. total numberof tota numberof errorsvar analogical og ca perspect perspective, ve there appears n cons verbal and nonverba nonverbalstress ass benefitt in to be no ssignificant assignment gnmentas considering der ngverba gn f cant benef remainder nderof of th thiss art article, c e therefore therefore, suggests. In the rema separateprocesses, as Roca (1988) suggests separateprocesses wholee are cons of the corpus as a who considered. dered the resu results tsof about 94% oof the ablee too correc he sstress ress on abou 55. INITIAL RESULTS.AML iss ab RESULTS he correctlyy ass assign gn the most frequentSpan words, and the words that are incorrectly ncorrect yass assigned gned stress by frequent Spanish sh words treatedas except well. AML are genera that traditional t ona ana analyses yses have treatedas exceptional ona as we generallyy those thattrad n stress ass ther have That is, s 80 assignment gnment occur on words that eeither 80.1%o 1%oof the errors in n a vowe that have ffinal na stress andend and end in vowel or ss, or thathave that have penu stress, or thathave penultt antepenulttstress antepenu n a consonantotherthan stress and end in consonantother than ss. Whatth What thiss indicates nd cates iss that ana analogy ogy 'recogrecogwithout thout hav nizes' n zes stress patternsw having ng to extrapo oba genera generalization zat onabout the extrapolate atea gglobal n the form of a ru rule. e data in AML iss aalso so qu s for examp esh ng out subpatterns quite te adept at ffleshing subpatterns.There is, example, e a fa fairly ry n --ico(s) thatend in andhave antepenu words,ma co(s) or --ica(s) ca(s) andhave large argegroup groupof words mainly n yadject adjectives, ves thatend antepenultt stress (e marked status of antepenu (e.g. g pub publico co 'public'). pub c ) In sp spite te of the 'marked' antepenulttstress stress, 99 out of 107 of these words are correct stress. In contrast correctlyyass assigned gned antepenu antepenulttstress contrast,aall 7 n --ica verbal forms that end in verba ca (e (e.g. g ssignifica, gn f ca cr critica, t ca ded dedica) ca) were correct correctlyyass assigned gned stress. penultt stress penu In sp AML'ss ab critic t c may arguethatAML spite te of AML ability ty to correct correctlyyass assign gn stress stress, a cr argue that AML iss not an accuratemode accuratemodel of Span stress assignment ts success rate iss not one because its Spanish shstressass gnmentbecause hundredpercent.77Ru hundredpercent Rulee mode modelss appearto suited ted to account appearto be much better su accounting ngfor for aall the data nce they can be formu formulated atedin n such a way as to accountcorrect account correctlyyfor for one data, ssince hundredpercentof the data hundredpercentof data. Wh Whilee th thiss iss true true, one must ask whatru what rule-based e-basedaccounts accounts must do to ach achieve eve such accuracy accuracy. To account for except exceptional ona patternsand patternsand vary varying ng ruleemode modelss mustmakeuse must make use of forma degrees of regu degreesof regularity, ar ty ru formalmechan mechanisms smssuch such as extraand other abstractformalisms metricality, metr ca ty odd morpho morphological og ca pars parsings, ngs andotherabstractforma smsthat that in n essence serve as d diacritics acr t cs (Farre Gilliss et aal. 1993) (Farrell 1990 1990, G 1993). The use of such forma formalisms sms iss n theor common in theories esof of competenceand competenceand linguistic ngu st cstructurebutthe structure,but theirrstatusas statusas psycho psycholoowhetherthey have actua ggical ca mechan mechanisms, sms and whetherthey actual corre correlates atesin n the m minds nds of speakers speakers, iss h highly gh y quest questionable onab e(Edd (Eddington ngton1996) 1996). It wou would d be poss constructa ru possible, b e however however, to constructa rule-based e-basedaccountw account without thoutd diacritics. acr t cs Such an accountwou account would d ssimply thatwords end n a vowe mp y state thatwords ending ng in vowel or s are stressedon stressed on the penu whilee those end n a consonant penultt sy syllable, ab e wh ending ng in consonant, except ss, rece receive ve ffinal na stress stress. The app of these ru rules es to the items application cat onof tems in n Tab Tablee 1 wou would d yyield e d 648 errorsfor errors for a success rateof rateof 86 which ch fa fallss farshortof far shortof AML 86.6%, 6% wh AML'ss 94 94.4% 4%success success rate rate. If antepenu antepenultt words are d mbs to 91 discounted, scounted the rate cclimbs 91.8% 8% for the ru rulee account account, and to 97 97.6% 6% in n the AML ssimulation. mu at on In eeither ther case case, AML appearsmore appearsmore adept at ass assigning gn ng stress correctly. correct y 66. EMPIRICAL EVIDENCE. EVIDENCE In ?4 ?4, we saw that ha the he ana analogical og ca cons consistency s ency oof Span Spanish sh stress ass assignment gnmentiss qu quite te h high. gh Wh Whilee ana analogical og ca cons consistency stency iss emp employed oyed as a test of performanceof a language performanceof anguage process processing ng mode model (e (e.g. g Dae Daelemans emanset et aal. 1994) 1994), there are 7I cou d be coun ered ha peop e do no nvar ab yproduce he expec ed ormse her see Berko 1958 Schn zer 1996 SPANISH STRESS ASSIGNMENTWVITHIN AML 103 that some linguistic behaviors orshave have a low conceivable vab e thatsome ow degree others, and itt iss ent others ngu st c behav entirely re y conce of cons would d not have a great tems wou case, many ssimilarly consistency. stency In that case m ar ybehav behaving ng items n common deal of featuresin dea and would d not serve as ana other:there would d common, andwou analogs ogs for each other:therewou is s be a great dea in n In this th s not nce aall deal of irregularity the AML, system. AML system rregu ar ty problematic, prob emat c ssince known items tems are stored as individual units ts in ex con Therefore nd v dua un n the menta mental lexicon. Therefore,another test of AML iss whetheritt he formation onof of evidence, dence such as the format helps ps exp explain a n emp empirical r ca ev of the and andh historical stor ca data,sslips neologisms, neo og sms language anguageacqu ps tongue, tongue acquisition s t ondata developments, deve opments evidence dence may be foundfor found for Span resulting resu t ngfrom language anguageusage usage. Such ev Spanish sh stress ass assigngnment. ment STUDYOFNEOLOGISMS OFNEOLOGISMS. 66.1. 1 ASKE ASKE'S SSTUDY Most words end Mos n -n n have final na sstress, which ch ress wh ending ng in iss why generat derive veffinal na stressas stress as the unmarkedcase for suchconsonantsuch consonantgenerative veana analyses yses der ffinal na words words.88Aske Aske (1990:35) noticed cedthat that in n Span about62% of 55 comhowever, not (1990:35), however Spanish, sh about62%of n -en have penu mon nonverbs end test ) ending ng in rgen 'virgin', penultt stress (e v rg n exadmen'test'). (e.g. g vvirgen Thiss contrastsw Th th 135 common nonverbsthat contrasts with nonverbs that end in n anothervowe anothervowel p plus us n (V(-e) (V(-e), which ch have stress on the ffinal na sy cancion on 'song', syllable ab e (e (e.g. g canc song segun 'according accord ng 90%oof wh to'). to ) Aske hypothes thatwhen a speakeriss faced w with th mak decision s on aboutwhere aboutwhere hypothesizes zesthatwhen making nga dec to stressanunfam stressan unfamiliar arwordend n -n makeuse of eeither thergenerat -n, the speakermay ending ngin speakermay makeuse generativeverules es or ana determine nestress stress p Generative veru rules es wou would d ass assign gn aall placement. acement Generat analogy ogy to determ type ru -n-final -n-f na wordsf words final na stress nce wordsthatareunfam words thatare unfamiliar arto to the speakercou dnothave not have speakercould stress, ssince been prev markedas except ex cons for However, iff speakerssearchedthe speakerssearchedtheirrlexicons exceptions. ons However previously ous ymarkedas words ssimilar m arto to those in n quest the stress of the word(s) accessed by applied edthe question, on and app the search would d be less ess likely receive ve ffinal na stress than -V(e)n words. ke y to rece search, -en words wou V e n words In order to test h hiss hypothes devised sed ssixx ffinal na -en nonce words and ssixx hypothesis, s Aske dev words. He then embeddedthem embeddedthem in n sentences in n wh which ch they appearedin n -V(.e)n V e n nonce words a nonverba nonverbal context and asked Span them. The sentences were speakers to read them Spanish sh speakersto etters S Since nce Span accent orthographyallows ows wr written ttenaccent Spanish shorthographya onlyy cap capital ta letters. presentedusing presentedus ng on marksto be de marksto deleted eted over cap thiss presentat edfor therebycontrolled for any effect of presentation ontherebycontro capitals, ta s th a wr written ttenaccent accent mark mark. The resu results tscclearly favor the ana model. Of the responsesto responsesto h hiss -V( analogical og ca mode ear y favorthe -V(.e)n e)nwords words, 96.8% 96 8% favoredf favored final na stress whilee on 55.6% 6% of the responsesto received ved responses to -en words rece onlyy 55 stress, wh ffinal na stress (1990:37) na rulee thatp that places aces ffinal ear y not app applying y ngaa ru subjects were cclearly (1990:37). The subjectswere stress on aall -n ffinal stresson na words words. The cclose ose re between the preferredstresspatterns preferredstresspatterns relationship at onsh pbetweenthe and the stress patternsthat ex exist st in n actua actual words suggests that stress ass was assignment gnmentwas determined determ nedon on the bas basiss of ssimilar m arwords words thatwere that were known to the subjects subjects. attributes butesh hiss ffindings hiss exper experiment mentwas was not based on nd ngs to ana analogy, ogy h Although A though Aske attr model of ana thereforeof interest nd ngs to determ determine neiff h hiss ffindings nterestto analogy. ogy It iss thereforeof any spec specific f c mode can be supportedby AML. To th thiss end tems twelve ve nonce items analysis ys s based on AML end, the twe supportedby an ana from Aske Aske'ss study were processed us results ts bed in n ?3 ?3. The resu using ng the databasedescr database described n Tab Tablee55. Wordsend n -en and those in V ee)n different fferent are assigned gnedqu quite ted -en, andthose n -V( n areass ending ng in appearin hiss exper out. A All -en words were asexperiment mentbore bore out hypothesized, zed and h patterns,as Aske hypothes patterns stress, wh whilee aall but one of the -V(e)n na received ved ffinal V e n words (seboran) rece ssigned gned penu penultt stress stress. stress In the AML ssimulation, assume that the behav probahighest ghestpred predicted ctedprobabehavior orw with th the h mu at on I assumethatthe that none of the nonce items assigned gned n -en wou would d be ass tems end ending ng in meaning ngthatnone bility b ty app applies, es mean ffinal na stress stress. Aske Aske'ss subjects na stress on 55 55.6% 6% of the responses responses. predicted ctedffinal subjects, though though, pred 8 For examp e 60% o he po ysy ab c words end ng n n n he da abaseare s ress na LANGUAGE, VOLUME 76, NUMBER 1 (2000) 104 OF F PROBABILITY PROBAB L TYOF FINAL NAL OF PENULT PROBABILITY PROBAB L TYOF OF PROBABILITY PROBAB L TYOF STRESS STRESS ANTEPENULTSTRESS ANTEPENULTSTRESS NONCE ALL NONVERBS ALL NONVERBS ALL NONVERBS WORD WORDS ALONE WORDS ALONE WORDS ALONE besoren corumen petaben pe aben faden aden merasen gorquen seboran porubon petamin pe am n tedon edon sorquin sorqu n perasun .000 000 .989 989 .994 994 .011 011 .006 006 .000 000 .901 901 .995 995 .098 098 .005 005 .000 000 .387 387 .994 994 .610 610 .006 006 .000 000 .702 702 .983 983 .298 298 .017 017 .000 000 .827 827 .991 991 .173 173 .009 009 .000 000 .996 996 .998 998 .004 004 .003 003 .001 001 .946 946 .996 996 .052 052 .003 003 .000 000 .024 024 .169 169 .975 975 .830 830 .018 018 .015 015 .368 368 .983 983 .614 614 .000 000 .009 009 .211 211 .991 991 .789 789 .000 000 .178 178 .084 084 .822 822 .916 916 .008 008 .035 035 .330 330 .963 963 .662 662 words. words or Aske Aske'ss nonce ress p TABLE55. Probab placement acemen for Probabilityyoof sstress .000 000 .000 000 .003 003 .000 000 .000 000 .000 000 .002 002 .001 001 .002 002 .000 000 .000 000 .002 002 whilee tems (83 stress for ffive ve out of ssixx items na stressfor On the -V(.e)n (83.3%), 3%) wh V e n items, tems AML pred predicts ctsffinal thereforecaptures responses. AML thereforecaptures 96.8% 8% of the responses n 96 na stress in subjects preferredfinal the subjectspreferredf inherGiven venthevar the variability ab tynhertat ve yG but not quantitatively. tat ve ybutnotquant preferencesqualitatively the subjects subjects'preferencesqua estimation mat on m tedest that the AML databaseiss a limited with th the fact thatthe coupled edw n surveydata survey data, coup ent in that the ssimulation mu at oncapturesthe capturesthe sufficient c entthat ex con itt iss suff mental lexicon, speaker'ssmenta of a Span Spanish shspeaker dent ca numerically ca yidentical. major trend, and iss not numer majortrend n presented the nonce words in data. Aske presentedthe n the data possible b e confound in But there iss a poss verbs. never as verbs or nouns nouns, neveras adjectives vesor nterpretedas adject onlyy be interpreted could d on n wh which ch they cou contexts in because n the AML ssimulation mu at onbecause penultt stress in assigned gned penu It iss poss possible b e that seboran was ass g pon fueran, database (e.g. ponian, an fueran n the database(e neighbors ghbors in verbal ne ts verba nf uence of its of the heavy influence assigned gned twelve ve nonce words were ass possibility b ty aall twe thiss poss order to test th etc.). ) In orderto tuvieron, tuv eron etc the nonverbalcontexts database.In thiss way thenonverba n the databaseInth tems in the nonverbalitems onlyy thenonverba stressusing stressus ng on n tems in nonverbalitems th the nonverba matched with respond to were matchedw subjects were asked to respondto Aske'ss subjectswere Aske stress. penultt stress receive ve penu to rece continued nuedto conditions t ons seboran cont under these cond database.Even underthese the database to n compar comparison sonto stressed in ncorrect ystressed tem (petaben) was incorrectly additional t ona item Furthermore,an add Furthermorean placement acementggiven ven to seboran by why the stress p unclear earwhy preferences.It iss unc the subjects subjects' preferences However, the fact that an subjects. However by the subjects assigned gnedby with th that ass coincide nc de w AML does not co ends analogs ogs lends owed as ana tems are aallowed nonverbalitems onlyy nonverba occurs when on mismatch smatchoccurs additional add t ona m should d assignment gnmentshou nonverbalstress ass verbal and nonverba that verba hypothesissthat furthercredence to the hypothes furthercredenceto (?4.1). 1) not be treatedseparate treatedseparatelyy(?4 udy eelicited c ed words n her 1988 sstudy, Hochberg, in Hochberg OFACQUISITION. OFACQUISITION STUDY HOCHBERG'S S 66.2. 2 HOCHBERG various ous name var children drenname First, rst she had ch preschoolers. ers F patternsfrom preschoo stress patternsfrom different fferentstress with w th d heard,wh which ch were words they heard repeatnonce wordsthey nap book. Next they had to repeatnonce picture cturebook objects in was that hypothesisswas syllables. ab es Her hypothes stressed on d stressedon different fferentsy easier er too ress eas regular arsstress with h regu should d find hey shou a they hen (a) nd words w rules, es then ress ru earn sstress ac learn n fact did d in if ch children drend n words ress in regularize ar zesstress end too regu should d tend hey shou ress and (b) nonregular arsstress; b they with h nonregu han words w pronouncethan 1988 690 ress (1988:690) regular arsstress. with h regu n words w ress in rregu ar zesstress not irregularize should d no ress bu but shou nonregular arsstress, with w h nonregu gn f made ssignifidrenmade that children confirmed. rmed She found thatch partially a yconf was part hypothesisswas Hochberg'sshypothes Hochberg han on ress than rregu ar sstress with h irregular ruc ure chang ng errors on nonce words w cantlyy more sstructure-changing can ruc ure chang ng he sstructure-changing addition, on more oof the patterns.9 erns 9 In add ress pa regular ar sstress with h regu nonce words w rregu ar ress irregular. regular ar sstress han made regu ress than errors regu regularized ar zed sstress 9 S ruc ure chang ngerrorsare hose ha en a a s ress sh or an a era ono he CV ske e on SPANISH STRESS ASSIGNMENTWITHINAML 105 differs ffers somewhat somewhat. As w The errorana real words Hochberg eelicited c ted d with th analysis ys s for the rea the nonce words errors were made on irregularly words, more structure-chang rregu ar ystressed structure-changing ngerrorswere words thanon regu wordsthanon but therewas no ssignificant difference fferencebetween between words,buttherewas gn f cantd regularly ar ystressedwords the percentageof errorsthat errorsthat regu errorsthat conregularized ar zedstress and the percentageof errorsthat verted regu nto irregular stress. Hochbergconc concludes udes that regular arstress into rregu arstress The mos he d difference erencebe between ween the he imitated m a edand and spon dataa iss that most likely ha ke y exp explanation ana onoof the spontaneous aneousspeech speech da he sstress the he ch children drenhad mastered eredbo both h the ress sys whilee they had mas nd v dua excep did d Thus, wh system em and individual exceptions ons too it. Thus hey d find nd known irregular somewhat hardertoo say than he r familiarity with h these hanknown known regu hese rregu arwords somewha regulars, ars their am ar yw words enab enabled ed them with h nove hem aat least eas too sstress ress them hem correc confronted ron edw novel words in n contrast, ras when con correctly. y In con the he imitation m a ontask, he ch he rru rulee know children drenwere were led ed by their ask the 1988 698 knowledge edge too regu regularize ar zeirregulars. rregu ars (1988:698) An aalternative ternat veexp explanation anat onof her ffindings nd ngs iss poss possible b e from an ana analogical og ca standpo standpoint. nt Knownwords are storedaalong Knownwordsarestored inherent nherentstress stress The fact thatregu with w ththe their r regularizaar zaong pattern.Thefactthat pattern ttion on of irregulars could d be attr attributed buted rregu arsand irregularization rregu ar zat onof regu regulars arswas rough roughlyyequa equal cou to the same types of retr retrieval eva prob both of words affecting ng problems emsaffect types indiscriminately. nd scr m nate y Unknownwords ex ca entry would d adoptthe stresspatternsof the theirrne words, hav having ng no lexical entry,wou neighghbors. Of course bors this th s account is s if f it t can be that course, plausible p aus b eon onlyy proven analogy ana ogy makes errors that regu errorsthat regularize ar ze stress more often than itt ass rregu arpatterns patternsto regu assigns gns irregular regularly ar y stressed words. stressedwords 66.3. 3 HOCHBERG DATAIN AN ANALOGICAL DATAIN HOCHBERG'S S ANALYSIS.O ANALYSIS Of the he 44,970 970 ACQUISITIONAL wordsin n my database stressedusing AML'ss aalgorithm. According ng gor thmAccord ncorrect ystressedus ngAML database,277 were incorrectly to these data difficult ff cu t stressto stress to ass nce 59 59.9% 9%of of antepenult, t ssince assign gn correct correctlyyiss antepenu data, the most d the antepenu n the databasewere databasewere incorrectly stressed. On 4% of words Onlyy 66.4% ncorrect ystressed antepenulttwords in stressed on the ffinal stressedon na sy whilee penu stress yielded penulttstressy e ded the mproper ystressed stressed,wh syllable ab e were improperly lowest owest errorrate errorrate (1 Thiss same h so seen in n the errorrates errorrates hierarchy erarchyof d difficulty ff cu ty iss aalso (1.2%). 2%) Th from the three-and fromthe three- and four-year-o n Hochberg m tat onexper Fig. g experiment ment(1988:700 (1988:700, F Hochberg'ssimitation four-year-olds dsin 13). 13) Of the 277 errorsproducedby nvo ved a move from an irregular to a rregu arto AML, 220 involved producedby AML Thiss meansthat33 meansthat33.9% 9%of of the irregularly rregu ar ystressed dca). Th g acd to dca) regular regu arstresspattern(e pattern(e.g. items tems (n = 649) were regu regularly ar y errorsmade on regu contrast,on onlyy 54 of the errorsmadeon regularized. ar zed In contrast stresseditems tems (n = 4177 made them irregular pdpel), y yielding e d ng a 11.3% 3% rregu ar(e (e.g. g pape papel to pdpe 4177)10 10 madethem rate of irregularization. thiss iss prec patternthat Hochbergfound Hochbergfound precisely se y the patternthat again, n th rregu ar zat onOnce aga in n her imitated m tated speech study errorsregularized stressed ar zedirregularly rregu ar ystressed study, where 53% of the errorsregu nvo ved mak (1988: regular arstress stress irregular rregu ar(1988: making ng a regu onlyy 23% of the errorsinvolved words, and on words 696). 696) so d the errorratesaccord divided v dedthe errorrates according to the age of the subjects subjects.The error ngto Hochbergaalso rate on regu tems rema remained nedvvirtually ve subjects ages threeto three to ffive, rtua yunchangedfor unchangedfor aall subjectsages regular aritems but the errorrateon butthe errorrate on irregular items droppedfromthe four- to the ffive-year-olds (Figure gure ve-year-o ds(F droppedfrom the four-to rregu artems 11). One way of approx n AML iss by vary differences fferences in varying ng the numberof number of approximating mat ngage age d items tems in n the database(Derw child d at 1994). Exact Exactlyy how many words a ch (Derwing ng & Skousen 1994) a ggiven earned iss d difficult ff cu t to ascerta ascertain. n Based on severa several d different fferentest estimates, mates ven age has learned Aitchison A tch son (1994:169)assumesthatathree-year-o vocabspeakerhas anact an active vevocabdEng English shspeakerhas (1994:169) assumesthata three-year-old thousand words, wh whilee a ffive-year-old of active ve vocabu vocabulary aryof ve-year-o d has an act ulary u ary of about a thousandwords about three thousandwords thousandwords. In any event n orderto analogical og ca apevent, in order to determ determine neiff the ana could d accountfor accountfor the deve nto databasewas d divided v ded into developmental opmenta phenomena phenomena,the databasewas proachcou two ha and the ha halff conta the least remainndiscarded. scarded The rema east frequentitems tems was d containing n ngthe halves, ves andthe 10 The 144 monosy ab c ems were no nc uded LANGUAGE, VOLUME 76, NUMBER 1 (2000) 106 70 60 _ s 50 _ I40 ",20 11 10 Age 4, and 1/2 database Age 5, andEntireDatabase Hochberg,Irregular F Xf Hochberg,Regular AMLIrregular ^ AMLRegular * 1. Errorrates by age and numberof words in database. FIGURE ing half was assigned stressin a tenfoldcross-validationsimulationaccordingto AML's algorithm,and the errorrates were calculated. These results are also summarizedin Fig. 1. The leftmost group of bars in Figure 1 representsthe error rates of Hochberg's four-year-oldsubjects, and the errorrates that resulted when only the most frequent half of the database was included in the analogical experiment.The rightmost bars indicate the errorrates for Hochberg's five-year-old subjects, and the errorrates that occurred when 4,970 database items were included. In both studies, error rates on regularitems varied little, but the errorrates on irregularlystresseditems declined for older subjects. In the AML simulation, the rate also dropped when a larger mental lexicon was assumed.A proportionstest reveals thatthis dropis significant(Z-statistic =7.44, p < .01, 99% confidence interval .0676, .1384) Hochbergconcludesthather findings supportthe existence of rules thatassign stress. Nevertheless, the analogical account mirrorsher findings quite closely. The ability of an exemplar-basedmodel to accountfor stressplacementerrorsis not limitedto Spanish. Gillis et al. 1994 demonstrateshow stressplacementerrorsin Dutcharebetteraccounted for if stress is determinedby analogy to known words, than it is by postulatedstress rules. 7. CONCLUSIONS. My purposewas to determineto what extent Spanishstress placement could be handled within AML. The 4,970 most common Spanish words served as a model of the mental lexicon, and as test cases as well. About 94% of these words were correctly stressed by analogy: Extremely low frequency words were correctly stressedin about 92% of the cases. No significantimprovementwas observedif verbs and nonverbswere allowed to analogize only on membersof their own category. Since AML is a model of languageusage, the most importantfindings are those that involve actuallanguageuse. Althoughthe resultsarenot perfect,the analogicalaccount of stress assignmentwas found to mirrorquite closely the resultsof Aske's nonce word study and Hochberg's study of stress acquisition. 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