The Use of Natural Language to Ask
Questions in a Collaborative Hypermedia
Teaching Environment
Werner Winiwarter, Osami Kagawa, Shin’ichi Konomi, Yahiko Kambayashi
Department of Information Science, Kyoto University, Sakyo, Kyoto, 606-01 Japan
Abstract. In this paper we present VIENA Classroom, an education system in
which the teacher designs the teaching material as hypermedia documents and
presents them to the students within a collaborative environment. An important issue
for virtual teaching environments is the efficient processing of the students’
questions. We have developed a user-friendly question support facility that
overcomes many limitations of real classroom situations. We provide the students
with a natural language interface so that they can formulate their questions directly
in Japanese. The system applies the computed semantic representations to the
retrieval of answers from a FAQ knowledge base or to the gathering of semantically
equivalent new questions. One of the main obstacles to the efficient use of natural
language interfaces is the often required high amount of manual knowledge
engineering. Therefore, we support an adaptive interface architecture to automate
the acquisition of linguistic knowledge. For that purpose we apply existing linguistic
resources and methods from machine learning. We guide the assignment to the
correct semantic category by an XSE (eXtended Semantic Enumeration) tree, a
powerful model-based machine learning paradigm. It selectively activates linguistic
tests from semantic, syntactic, and pragmatic analysis according to the required
feature tests during the search of rule nodes. We build the XSE-tree automatically
based on past input questions without the need for any additional linguistic
knowledge.
1. Introduction
Hypermedia and collaborative environments are two compelling technologies that make it possible to
engage students in solving interesting tasks, which results in a more active participation and higher
motivation. Concerning the former there have already emerged a large variety of systems that use
hypermedia techniques to present the teaching material in a concrete and perceptually easy way as
well as to provide a rapid and convincing interaction and feedback (for example, see [1]). Regarding
collaborative environments only recently first prototypes have appeared that make use of the high
potential of collaborative education to solve more interesting and complex problems. Furthermore,
they promote communication skills and the articulation of arguments to other group members.
Examples of first successful systems are CoVis [2], SpeakEasy [3], or CSILE [4].
VIENA Classroom is a collaborative hypermedia teaching environment that has as important
feature a user-friendly question support facility to overcome many limitations of real classroom
situations. This is an essential tool to be able to scale to the demands of practicable use in realistic
virtual classroom settings. However, so far this topic is not covered by most of the existing systems.
We make use of a natural language interface so that students can formulate their questions directly in
Japanese. One of the main shortcomings of many existing natural language interfaces (for a recent
survey see [5]) is their static behavior. This prohibits an effective portation to new application
domains as well as the flexible adaptation to dynamic changes in the application domain. Therefore,
we supplement the interface with adaptive techniques in that we apply existing linguistic resources
and methods from machine learning to the automatic acquisition of linguistic knowledge (see also [6]).
In particular we control the semantic component of the interface by an XSE-tree; a data structure
learned from past input sentences without the need of any additional manual knowledge engineering.
For a more detailed presentation of the VIENA Classroom system see [7], for a more explicit
discussion of the applied language engineering techniques we refer to [8].
2. System Description
In a conventional classroom setting the teacher is confronted with the following problems concerning
the answering of questions: the limited number of questions that he can answer, considerable delay in
answering, the missing possibility to select relevant questions, and the need to answer the same type of
questions again and again. Therefore, one of our central issues was to supplement VIENA Classroom
with a powerful question support facility.
In VIENA Classroom a student always formulates a question in a specific context in that he
highlights the corresponding text passage in the teaching material. It is the responsibility of the teacher
to define a uniform context model during the design of the teaching material. With that, the students
can easily select the corresponding context by double-clicking in the concerned area. However, the
student is still free to mark a specific term or phrase; in such a case the interface resolves the question
according to the context. We collect and group new questions for each context based on their semantic
representation. For each group of semantically equivalent questions belonging to the same semantic
category the system computes a representative question as input for an informative display to the
teacher (see Sect. 3). Figure 1 shows an example translated into English; we mark original questions
with context resolution by an asterisk.
To answer a question, the teacher can choose between an off-line mode by clicking on the Answer
button and an on-line mode through the Connect button. By way of the Filter button the teacher can
configure the display of new questions as filter that gathers questions until the next answering period.
It can be defined based on a certain period, break-points in the teaching material, or thresholds on the
frequency data for new questions. Finally, the Edit button serves for editing purposes, for example,
the modification of representative questions, the merging or splitting of semantic categories, etc. The
result of the editing process is then made generally valid by rebuilding the XSE-tree (see Sect. 3).
Besides the use of FAQ frequency data for ranking new questions, other important uses are to provide
feedback for the revision of the teaching material and the automatic generation of context-sensitive
FAQ lists, which students can browse through before formulating a question.
New questions
CONTEXT
The virtual memory makes it possible to execute non-resident
programs on real storage. This realizes a larger process (space)
than physical storage.
REPRESENTATIVE QUESTIONS
NUMBER
What is the difference between process and space?
Which meaning does page table have?
What is the advantage of global replacement?
ORIGINAL QUESTIONS
10
5
3
QUESTIONER
What is the difference between process and space?
Werner Winiwarter
What is the difference to process? * What is the difference
Osami
Kagawa?
to process
What is the difference between space and process?
Yahiko Kambayashi
Answer
Connect
Filter
Edit
Fig. 1: Example of display of new questions
Quit
The physical organization of the VIENA Classroom system is a client/server architecture. The
teacher and students work on client machines whereas the record of lecture, the FAQ knowledge base,
and the history database are located at the server. Figure 2 shows the system architecture by giving
emphasis to the question support facility. The blackboard object reads the teaching material together
with the actions of the teacher and broadcasts it to the students, who process it by taking personal
notes. The students can access the record of lecture for later off-line review of the lectures; besides
that, we also use it for the extraction of domain-specific terms.
Concerning linguistic analysis, we process the Japanese questions locally to provide the required
personalization; the interfaces then send the semantic representations to the FAQ knowledge base to
retrieve the corresponding answers. The FAQ knowledge base gathers new questions and sends them
to the teacher; it also generates the FAQ lists and the FAQ data for the revision of the teaching
material. The teacher sends answers to new questions as well as updates of semantic categories to the
FAQ knowledge base whereas the students send requests by directly selecting items from the FAQ list.
Finally, we collect all information about past questions in the history database, which forms the basis
for the periodic reconstruction of the XSE-tree.
We have implemented the natural language interface by making use of the deductive objectoriented database system ROCK & ROLL (Heriot-Watt University, [9]). It supplies a powerful logic
and object-oriented programming language that enables the efficient realization of the complex
demands of the application. Especially the object-oriented inheritance mechanisms provide the
required hierarchical structure for the compact storage of the linguistic knowledge.
3. Natural Language Interface
The interface architecture displayed in Fig. 3 consists of the three main parts lexical, semantic,
and maintenance component. The lexical component is divided into three central modules: morpholexical analysis, UVL (unknown value list) analysis, and spelling error correction. Morpho-lexical
analysis performs the tokenization of Japanese input, that is, the segmentation into individual input
words. By accessing a domain-independent lexicon the module transforms the input into a deep form
TEACHER
teaching material blackboard (send)
STUDENTS
blackboard (receive)
FAQ lists answers
FAQ data
for revision of
teaching material
?
notes
domain-specific
natural lexicon
language
questions
new questions
XSE-tree
natural language
interface
SERVER
record of
lecture
FAQ knowledge base
history
database
Fig. 2: System architecture
LEXICAL COMPONENT
Teaching material
Extraction of domainspecific terms
Input
Morpho-lexical analysis
Domainindependent
lexicon
UVL-analysis
Domainspecific
lexicon
Spelling error correction
MAINTENANCE
COMPONENT
T
EDIC
JIDIC
KAN
Generation of new
lexical entries
Semantic analysis
XSE-tree
build
XSE-tree
XSE-tree
search
Syntactic analysis
Grammar
Pragmatic analysis
Incremental
adaptation
History
database
Concept
hierarchy
Paraphrasing
Focus
User
feedback
Semantic
representation
SEMANTIC COMPONENT
Fig. 3: Interface architecture
list (DFL), which indicates for each token its surface form, category, and a set of associated deep
forms. UVL-analysis deals with domain-specific terms contained in the input by generating a
corresponding unknown value list (UVL). This separation of the domain-independent from the
domain-specific terminology provides the basis for an easy portation of the interface to new
application domains. Finally, we support spelling error correction of domain-specific terms [10].
Besides these basic modules we make use of the following two linguistic resources [11]:
1. the freeware Japanese-English dictionary file EDICT as secondary lexicon for the retrieval of
information about unknown words and the automatic generation of new lexical entries,
2. the pronunciation data contained in the Japanese character file KANJIDIC to improve the
accuracy of spelling error correction.
The semantic component selects the correct semantic category for the input question. For that
purpose it operates on an XSE-tree that enables the automatic acquisition of the required knowledge
from past input data (see Sect. 4). By searching the XSE-tree, the activation of semantic, syntactic,
and pragmatic analysis is controlled according to the information stored in the XSE-tree. The
semantic analysis module accesses a concept hierarchy, which provides a hierarchical structure of
the semantic deep forms according to their similarities. It replaces the strict test whether the DFL
contains a deep form or not by calculating the length of the shortest path between the concerned deep
form and the elements of the DFL. This gives a more accurate characteristic for the degree of semantic
similarity and enables us to deal efficiently with the use of synonyms [12].
Syntactic analysis is only applied in situations where it is necessary for disambiguation. We
compute the syntactic structure of the input by using a powerful extension of the categorial grammar
formalism. It supplies the framework for a robust bottom-up parser that can also analyze incomplete
and ungrammatical sentences efficiently [13]. The module verifies the resulting parsing tree by means
of syntactic disambiguation rules to select the correct interpretation. Pragmatic analysis deals with
the fact that user input often refers to previously mentioned questions so that important information is
missing. Therefore, we have to extend the scope of analysis from the isolated question by keeping
track of the actual focus. We apply a simple but efficient method that checks the result of the previous
analysis whether it contains missing referenced domain-specific terms.
The output of the XSE-tree search is one of the following three possibilities: (1) the correct
semantic category, (2) one or several wrong semantic categories, or (3) several semantic categories
including the correct one. To eliminate case 2 and 3, we make use of a paraphrasing module that
generates the representative questions for the individual categories and displays them to the user. The
module produces the representative questions by means of templates that may contain variables to be
replaced by the domain-specific terms in the UVL. Whereas case 1 and 3 are trivial for the user, case
2 requires that the user either creates a new semantic category or that he selects an existing one from
the FAQ list. In the former case, the module derives the template for the new semantic category
automatically from the input question. The correct semantic representation produced by the
paraphrasing module is sent to the FAQ knowledge base to retrieve the corresponding answer or to
transfer the question to the teacher for later processing. Besides that, we also use it as input for an
incremental adaptation of the XSE-tree, especially concerning the insertion of new semantic
categories and the resolution of inconsistencies for case 2 and 3.
Finally, the last of the three main parts of the interface is the maintenance component. Whereas
the lexical and semantic component operate locally at the client sites to provide linguistic
personalization, the maintenance component is located at the server site (see also Sect. 2). Its two
main tasks are to extract domain-specific terms and to build the XSE-tree. As concerns the former, we
use the teaching material as input to a semi-automatic extraction tool to facilitate the process of
adding new domain-specific terms. By accessing the domain-independent and domain-specific lexicon
we first remove all corresponding items in the teaching material. The remaining text forms the basis
for a ranked list of candidates for new domain-specific terms. This represents a comfortable
assistance in inserting new entries into the domain-specific lexicon. The resulting updates are
validated by broadcasting them to the local lexicons at the client sites.
We also build the XSE-tree globally based on a large number of past input sentences collected
from all client sites. The FAQ knowledge base gathers this data and stores it in a history database at
the server site. As reasonable compromise between global consistency and local flexibility we have
chosen the above-mentioned solution of allowing local incremental adaptations of the individual
XSE-trees. The general update of the local XSE-trees is then performed periodically by the activation
of the central XSE-tree build module.
4. XSE-Trees
XSE-trees are an extension of the SE-tree approach from machine learning [14] that generalizes
decision trees. It is especially well suited for domains with few training examples and noisy
environments. Different from ordinary SE-trees, XSE-trees are implemented in a deductive objectoriented framework, which makes it possible to replace simple feature tests by complex method
invocations. With that, we can activate the required modules of semantic, syntactic, and pragmatic
analysis during the traversal of the XSE-tree.
We build the XSE-tree based on past input data by using the algorithm displayed in Fig. 4. For
this we first sort the features f according to a sequence function seq(f). It takes account of the
relevance as well as of the cost of deriving the different types of linguistic knowledge. For that purpose
it uses the following priority classes for the sequence of performed tests: (1) number of domainspecific terms in the UVL (|UVL|), (2) semantic deep forms in the DFL (FDFL), (3) syntactic
disambiguation rules, and (4) pragmatic focus. We expand each node of the XSE-tree only for the
features of the same priority class, which also reduces the initial branching factor significantly. We
store the following data structures for the individual nodes:
1. path: set of feature tests on the traversal from the root to the node,
2. set of covered questions: all questions that satisfy the feature tests in the path,
3. view: contains all features f of which seq(f) is greater than seq(x) of any feature x in the path,
4. child node list: list of all child nodes for further expansions of the path.
A candidate expansion is impotent if (1) Q’ is empty, if (2) Q’ equals Q, or if (3) all questions in
Q’ agree on their feature values for the features in V’ but there is no agreement on the assigned
semantic category. A rule is then a candidate expansion where all questions in Q’ belong to the same
semantic category. Finally, a candidate expansion P’ is subsumed by a rule P’’ if P’’⊂ P’.
Besides the assignment of the derived semantic category to rule nodes, we use for all other nodes
the most frequent semantic category in Q as default solution. It is applied if none of the feature tests of
the child nodes is successful. For the feature test regarding the pragmatic focus we have to add a
special treatment because the interface needs the information retrieved from the focus to construct the
semantic representation. Therefore, we add a corresponding child node even if the node in question is
a rule node. The syntactic disambiguation rules to be applied are either taken from the information
about the past input, if the ambiguity has already occurred at the client site, or they are derived from
a list of possible rule applications.
Figure 5 gives a detailed example of the construction of a simple XSE-tree based on 10 input
sentences. For each sentence we indicate the mapping to the correct semantic category, the applied
linguistic knowledge, and the corresponding template. For the DFL we only consider one-sided tests
concerning the deep forms of nouns and verbs (except auxiliaries). Finally, the syntactic
disambiguation rule is a two-sided test that checks whether the domain-specific term dominates the
interrogative pronoun in the parsing tree or not.
Program Build-XSE
begin
create new root node R at level 0;
retrieve the complete set of past questions Q;
assign Q to the set of covered questions of R;
retrieve the complete set of features F;
assign F to the view and { } to the path of R;
invoke method EXPAND for R;
set current level L to 1;
repeat
foreach node N at L do
foreach C in child node list CNL of N do
if C is not a rule node then
begin
invoke method EXPAND for C;
if child node list of C is empty then
begin
remove C from CNL;
delete C;
end
end
increase L by 1;
until no more successful expansion;
end
Method EXPAND for node N
begin
foreach feature F in view V of N do
if child node list CNL is empty or
F belongs to same priority class as the feature tests in CNL then
foreach possible value X of F do
begin
derive candidate expansion P’ from path P of N: P’ = P ∪ {F=X};
restrict set of covered questions Q of N to Q’: Q’ = {q∈Q|F=X};
restrict V to V’: V’ = {f∈V|seq(f)>seq(F)}
if P’ is not impotent and
is not subsumed by any existing rule then
begin
create new child node N’ at next level;
assign Q’, P’, V’, and feature test F=X to N’;
insert N’ into CNL;
if P’ is a rule then
begin
mark N’ as rule node;
insert P’ into set of discovered rules;
end
end
end
end
Fig. 4: Algorithm to build XSE-tree
Fig. 5: Example of XSE-tree
5. Conclusion
In this paper we have presented the VIENA Classroom system, which aims at providing a new kind of
teaching environment that goes beyond the limitations of traditional lectures. In particular we have
addressed the problem of user-friendly question support, an important feature on the way to a more
active participation and higher motivation of students.
We have just finished the implementation of the adaptive natural language interface; the
complete implementation of the other components of the education system is still in progress. So far
we have developed several prototypes of limited functionality to show that the chosen architecture is
feasible to deal with the high demands of the application. First limited test sessions resulted in very
positive reactions from the students and a sharp reduction of the hesitation to ask questions.
Future work will concentrate on the important issue of designing and applying an appropriate
evaluation scheme to measure the success of our system. The evaluation will also show how the new
environment contributes to the emergence of new teaching and learning styles and in particalur how
the use of question support improves the quality of education. Proceeding from these results we plan
to develop a revised version of our system for public use through the World-Wide Web. We believe
that VIENA Classroom will become a valuable tool for virtual distance education, which will
represent a tempting alternative to traditional text-book lessons by making the classes more
entertaining and enjoyable.
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