Multilingual adaptations of ANNIE, a reusable information
extraction tool
Diana Maynard, Hamish Cunningham
Dept of Computer Science
University of Sheffield
Sheffield, Si 4DP, UK
[email protected]
Abstract
In this demo we will present GATE, an architecture and framework for language engineering, and ANNIE, an information extraction system developed within it. We will demonstrate
how ANNIE has been adapted to perform NE
recognition in different languages, including Indic and Slavonic languages as well as Western
European ones, and how the resources can be
reused for new applications and languages.
• tools for performance evaluation
• support for manual annotation
• reusable visualisation components
• database support (Oracle, PostgresQL)
• support for distributed resources from the
Web
• comprehensive document format support
(SGML, XML, HTML, RDF, email, plain
text)
1 Introduction
GATE' is an architecture, development environment, and framework for building systems
that process human language (Cunningham et
al., 2002; Maynard et al., 2002). It has been
in development at the University of Sheffield
since 1995, and has been used for many R&D
projects, including Information Extraction in
multiple languages and media, and for multiple
tasks and clients. GATE is available freely, as
an open source system, under the GNU library
licence, and has been downloaded by around
2500 sites worldwide. The core architecture and
some applications developed within GATE have
been previously demonstrated (Cunningham et
al., 2002); however, this demonstration will focus on the multilingual aspects of GATE, and
adaptations of its IE system for different languages.
Version 2 of GATE has a large number of
added features from the previous version, such
as:
• comprehensive multilingual support via
Unicode
'This work has been supported by the Engineering
and Physical Sciences Research Council (EPSRC) under grants GR/K25267 and GR/M31699, and by several
smaller grants.
Gate Unicade Editar
-
Unicade Sampler-tat
File Edit Options Help
H
IL IC[411 'Anal UrocodeirIS
Arabic:13.0 3 zp- J.11 JSI
J
9 ■
S JI9 U.
Armenian: tipEnni Duval& lau.tht 11.6.241 laUlnalagbula
Chinese: 1-kkl',3
tT•.-ftals, c.
Farsi I Persian:j3
66).5. 863033.
Georgian: 8063b 3368
Hebrew: -.5 pnn s5 no royipt 5m5 513 -. 'PC
Hindi: 4 isbi T 1tsd I
31 t4tsi d4f
Japanese: f1, 14/37...tIt's:641 -1- 0
Korean: 1-1- L -W-21ff
VoiR.
Marathi: 4t ti -1 /9113) 9I-Wa, 1:MT TA-d- ff41- .
Pashto: .4149....
aseP
vITH I
Sanskrit:
Thai: iuAttn9t.- anlei
Urdu: -bcej31Las"
Yiddish:UEP1 -rn Lou DY px w5a Inv VP TS.
-t-tiafokt*-)(-mth..„
Figure 1: Unicode text in Gate2
2 Processing Resources
GATE provides a baseline set of reusable and
extendable language processing components for
common NLP tasks, known collectively as ANNIE (A Nearly New Information Extraction
System). These include a Unicode tokeniser,
sentence splitter, POS tagger, gazetteer, semantic tagger, name coreferencer (orthomatcher)
219
and pronominal coreferencer. For more details,
see (Cunningham et al., 2002). ANNIE currently produces precision and recall figures for
named entity recognition of around 90%, depending on the text type.
An online demo of ANNIE is available at
http://gate.ac.uk/a,nnie/index.jsp . A set of
movies demonstrating document and corpus
loading, processing and storing, manual annotation of documents and corpora, creating, running, saving and restoring applications and viewing their results is available at
http: / /www.gat e. ac. uk/ demos / movies .ht ml.
3 Multilingual support - the GATE
Unicode Kit
GATE is one of the few architectures to support multilingual processing, using Unicode as
its default text encoding. A Unicode enabled
graphical user interface (GUI) needs to address
two main issues: the capability to display text
and the ability to enter text in other languages
than the default one.
It also provides a means of entering text in
a variety of languages and scripts, using virtual
keyboards where the language is not supported
by the underlying operating platform (Java itself does not support input in many languages
covered by Unicode, although it supports Unicode representation). Figure 1 depicts text in
various scripts displayed in GATE. The facilities have been developed as part of the EMILLE
project (Baker et al., 2002), designed to construct a 63 million word corpus of South Asian
languages. There are currently 28 languages
supported in GATE, and more are planned for
the future. Since GATE is an open architecture,
new virtual keyboards can easily be defined by
users and added to the system.
Apart from the input methods, GUK also
provides a simple Unicode-aware text editor
which is important because not all platforms
provide one by default or the users may not
know which one of the already installed editors is Unicode-aware. Besides providing text
visualisation and editing facilities, the GUK editor also performs encoding conversion operations. The editor has proved a useful tool during the development and testing of GATE in a
cross-platform environment, while the ability to
handle Unicode enables applications developed
220
within GATE to be easily ported to new languages.
4 The future isn't English
Robust tools for multilingual information extraction are becoming increasingly sought after now that we have capabilities for processing
texts in different languages and scripts. While
the default IE system is English-specific, some
of the modules can be reused directly (e.g.
the Unicode-based tokeniser can handle IndoEuropean languages), and/or easily customised
for new languages (Pastra et al., 2002). So far,
ANNIE has been adapted to do IE in Bulgarian,
Romanian, Bengali, Greek, Spanish, Swedish,
German, Italian, and French, and we are currently porting it to Arabic, Chinese and Russian, as part of the MUSE project 2 .
IMMFis AnnotatiooidA
Din surnarui Nr.336/1111110.2002
• Gnidul calatoriilorfa4a vita in ball Bilandul activitadii poobor buzodani in anul
111.(partea b•Polidistii au posibOtatea legislativa de aid indeplini mai blue
sarcinileii misnimle cc le revin• A incep-ut cnn nou an de lupta cu infractorii: Printre
primii la calatorli fara vita, 5eribedn Nadine din
a font prinsa pa aerop-ort
incercand nO fuga de datorii neonorate de
Un het
circa
din biserici
font intuit tocmai de pazni-cul care l-a prins/S-a spart biblioleca Casei de culkira
dar hodii RIJ s-au afire de nici o carte/Explozie la sonda 304 Grajdana •Jurnal rutier
- Ultima zi din an sub semnul imprudendei Somne papilare" de Col Nicotae
Rutaru • "kitrnet pas cu pas" de Adrian firlacdru4-Tanasescu•invataturi din
arhivele brancovenes.ti•
MO -AMA PERFOPMANTEI "Este momentui sa ne redescoperm pasiunea _1We
Date
5e4
Romanian
20
_BLOCK s
34 kind= date, rule= DateOnlyFinal)
Location
Romanian
73
Date
ilkoutan I an
132
136 {kind= date, rure-DateOnlyFinal)
Person
Romanian
334
349 {rule= PersonFinal}
Location
Romanian 354
359 {rule= LocFinal}
Money
451 {kincl=number, rule=MoneyCurrency
Person
Romanian•434
_
Romanian 707
Person
Romanian
771{ruie.Per$onFl}
'MOW
754
p.
79 Me= LocFinal}
726 {rule=PersonFinal}
Figure 3: Romanian news text annotated in
GATE
4.1 NE in Slavonic languages
The Bulgarian NE recogniser (Paskaleva et al.,
2(02) was built using three main processing resources: a tokeniser, a gazetteer and a semantic
grammar built using JAPE. There was no POS
tagger available in Bulgarian, and consequently
we had no need of a sentence splitter either.
The main changes to the system were in terms
2http://www.des.shefac.uk/research/groups/n1p/muse/
DA Gate 2.0alphal
File Edit Tools Help
di BO system
al Gate
Messages I
17-1111 Applications
Emol- apgslee aerompe
CI.
ej
,
I
e
E <Default>
H8 crepes
BG system
Language Resources
=
Text BO
8 METO4HEI EBFIOna AHM,I9
A Text EiG
1—T7 Lookup
reproahuln m chpaagnn ca e Sanagaa Espana.
F
tR- I'M K IHNHI. X8M
SpaceToken
Processing Resources
BG Names
t".■ Unicode Tokeniser
=
BG Gazetteer
g Data stores
30 Mavr 2000
D -r
Dr.
—
M Cunningham
M
Cunningham
D-rAHrenoaa
Type •
Set
Stag
End
Features
Lookup
,Default,
71
78
trninorType=country, majorType=location}
Lookup
,Default>
60
68
trninorType=country, majorType=location}
Lookup
,Default>
37
43
trninorType=country, majorType=location}
Lookup
,Default>
0
8
trninorType=country, majorType=location}
Person
,Default>
210
216
tkInd=personName, rule=KallnaPersonl
Person
,Default>
173
179
tkInd=personName, rule=HamishPerson}
Person
,Default>
148
154
tkInd=personName, rule=HamishPerson}
Person
,Default>
108
113
tkind=personName, rule.HamishPersonBet
Person
,Default>
266
270
tkind=personName, rule=GaliaPersont
Person
,Default>
248
253
tkind=personName, rule=GaliaPersont
Person
,Default>
230
236
tkind=personName, rule=KalinaPersonl
Figure 2: Bulgarian named entities in GATE
of the gazetteer lists (e.g. lists of first names,
days of the week, locations etc. were tailored for
Bulgarian), and in terms of some of the pattern
matching rules in the grammar. For example,
Bulgarian makes far more use of morphology
than English does, e.g. 91% of Bulgarian surnames could be directly recognised using morphological information. The lack of a POS tagger meant that many rules had to be specified in
terms of orthographic features rather than parts
of speech. Figure 2 shows some Bulgarian text
annotated in GATE.
Since the structure of the Bulgarian and
Russian languages is quite similar, we anticipate that converting the Bulgarian system
to Russian will be fairly straightforward, and
will involve mostly replacing and/or updating
gazetteer lists at least to obtain comparable
results.
4.2 NE in Romanian
The Romanian NE recogniser (Hamza et al.
2002) was developed from ANNIE in a similar way to the Bulgarian one, using a tokeniser,
gazetteer and a JAPE semantic grammar. Figure 3 shows some Romanian text annotated in
GATE.
Romanian is a more flexible language than
English in terms of word order; it is also agglu;
tinative e.g. definite articles attach to nouns,
making a definite and indefinite form of both
common and proper nouns.
As with Bulgarian, the tokeniser did not need
to be modified, while the gazetteer lists and
grammar rules needed some changes most of
which were fairly minor. For both Bulgarian and Romanian, the modifications necessary
were easily implemented by a native speaker
who did not require any other specialist skills
beyond a basic grasp of the JAPE language and
the GATE architecture. No Java skills or other
programming knowledge was necessary. The
Gate Unicode kit was invaluable in enabling the
preservation of the diacritics in Romanian, by
saving them with UTF-8 encoding.
;
4.3 NE in other languages
ANNIE has also been adapted to perform NE
recognition on English, French and German dialogues in the AMITIES project 3 , a screenshot
of which is shown in Figure 4. Since French and
German are more similar to English in many
ways than e.g. Slavonic languages, it was very
easy to adapt the gazetteers and grammars accordingly.
3
http://www.dcs.shcf.ac.uk/nlp/amitics
221
M ArruChat
7EIEF
File
AMITIES: Amities, how can I help, je vous ecoute, was lcann ich ftir Sie tun?
USER: Guten Tag, Ich mochte gem wissen, movie! ich out metnem Konto hate
AMITIES: Darf ich bitte Ihre Kontonummer haben?
USER: Mon numero est 5522333344945555
AMITIES: Pouvez-vous me donner votre adresse et votre date de naissance, s'il
vous plait?
USER: I live at 547 Ratchet Lane, and my date of birth is the 18th of May, 1969
AMITIES: Thank you. Here is the information about your account.
Figure 4: AMITIES multilingual dialogue
4.4 Surprise languages
We are currently investigating methods of
adapting ANNIE to new languages with the
minimum of resources and time. Our previous
experiments with languages other than English
have demonstrated that we can get reasonable
results in around 2 person months using a native speaker and hand-coded semantic tagging
rules, without requiring resources such as dictionaries or POS taggers for that language. We are
also planning participation in the TIDES-based
"surprise language experiment", which requires
various NLP tasks such as IE, IR, summarisation and MT to be carried out in a month on
a surprise language, the nature of which will
not be known in advance. The open and flexible architecture of GATE, and the separation
of linguistic data from processing makes it an
ideal environment within which to perform such
a task. Any available linguistic resources such
as dictionaries and POS taggers can be simply
plugged into the model, but if these are not
available we can simply modify other components as necessary.
5 Conclusion
In this demo we have shown how an existing set
of IE tools has been modified to a diverse set
of languages with minimum overhead. The advantage of having such low-overhead portability
is that it enables quick deployment of IE tools
with acceptable performance, which, even if not
developed in end-used applications can be used
to bootstrap the creation of IE-annotated corpora and/or facilitate the training of learning
tools for adaptive IE. In addition, some adaptive IE tools are now using the ANNIE corn;
222
ponents to provide them with richer linguistic
information (Ciravegna et al., 2002).
References
P. Baker, A. Hardie, T. McEnery, H. Cunningham, and R. Gaizauskas. 2002. EMILLE,
A 67-Million Word Corpus of Indic Languages: Data Collection, Mark-up and Harmonisation. In Proceedings of 3rd Language Resources and Evaluation Conference
(LREC'2002), pages 819-825.
F. Ciravegna, A. Dingli, D. Petrelli, and
Y. Wilks. 2002. User-System Cooperation in
Document Annotation Based on Information
Extraction. In 13th International Conference on Knowledge Engineering and Knowledge Management (EKAW02), pages 122137, Siguenza, Spain.
H. Cunningham, D. Maynard, K. Bontcheva,
and V. Tablan. 2002. GATE: A Framework
and Graphical Development Environment for
Robust NLP Tools and Applications. In Proceedings of the 40th Anniversary Meeting of
the Association for Computational Linguistics.
0. Hamza, D. Maynard V.Tablan, C. Ursu,
H. Cunningham, and Y. Wilks. 2002. Named
Entity Recognition in Romanian. Technical report, Department of Computer Science,
University of Sheffield.
D. Maynard, V. Tablan, H. Cunningham,
C. Ursu, H. Saggion K. Bontcheva, and
Y. Wilks. 2002. Architectural elements of
language engineering robustness. Journal of
Natural Language Engineering - Special Issue on Robust Methods in Analysis of Natural
Language Data, 8(2/3):257-274.
E. Paskaleva, G. Angelova, M.Yankova,
K. Bontcheva, H. Cunningham, and
Y. Wilks. 2002. Slavonic named entities in gate. Technical Report CS-02-01,
University of Sheffield.
Katerina Pastra, Diana Maynard, Hamish Cunningham, Oana Hamza, and Yorick Wilks.
2002. How feasible is the reuse of grammars
for named entity recognition? In Proceedings of 3rd Language Resources and Evaluation Conference.
;