Integration of a Lexical Type Database with a Linguistically Interpreted
Corpus
Chikara Hashimoto,‡ Francis Bond,∗ Takaaki Tanaka,∗ Melanie Siegel§
‡ Graduate School of Informatics, Kyoto University
∗ Machine Translation Research Group, NTT Communication Science Laboratories
§ Language Technology Lab, DFKI
‡hasimoto@pine.kuee.kyoto-u.ac.jp
∗{takaaki,bond}@cslab.kecl.ntt.co.jp
§siegel@dfki.de
Abstract
We have constructed a large scale
and detailed database of lexical types
in Japanese from a treebank that in-
cludes detailed linguistic information.
The database helps treebank annota-
tors and grammar developers to share
precise knowledge about the grammat-
ical status of words that constitute the
treebank, allowing for consistent large
scale treebanking and grammar devel-
opment. In this paper, we report on
the motivation and methodology of the
database construction.
1 Introduction
Treebanks constructed with detailed linguistic in-
formation play an important role in various as-
pects of natural language processing; for exam-
ple, grammatical knowledge acquisition; world
knowledge acquisition (Bond et al., 2004b);
and statistical language model induction. Such
treebanks are typically semi-automatically con-
structed by a linguistically rich computational
grammar.
A detailed grammar in turn is a fundamen-
tal component for precise natural language pro-
cessing. It provides not only detailed syntactic
and morphological information on linguistic ex-
pressions but also precise and usually language-
independent semantic structures of them.
However, such a deep linguistic treebank and
a grammar are often difficult to keep consistent
through development cycles. This is both because
multiple people, often in different locations, par-
ticipate in a development activity, and because
deep linguistic treebanks and grammars are com-
plicated by nature. Thus, it is often the case that
developers lose sight of the current state of the
treebank and grammar, resulting in inconsistency.
We have constructed a linguistically enriched
treebank named ‘Hinoki’ (Bond et al., 2004a),
which is based on the same framework as the
Redwoods treebank (Oepen et al., 2002) and uses
the Japanese grammar JACY (Siegel and Ben-
der, 2002) to construct the treebank.1 In the con-
struction process, we have also encountered the
problem just mentioned. We are aiming to re-
solve this problem, which we expect many other
project groups that are constructing detailed lin-
guistic treebanks have encountered. Our strategy
is to take a “snapshot” of one important aspect of
the treebank and grammar for each development
cycle. To be more precise, we extract informa-
tion about lexical items that are being used in tree-
banking from the treebank and grammar and con-
vert it into an electronically accesible structured
database (the lexical-type database). Such a snap-
shot, the database, certainly helps treebank anno-
tators and grammar developers to share precise
and detailed knowledge of the treebank and gram-
mar and thus to make them consistent throughout
the development cycle.2
Lexical items whose information is included
1Currently, the Hinoki treebank contains about 121,000
sentences (about 10 words per sentence).
2We think we also need another snapshot, that of the
grammar rules and principles being used. In this paper, how-
ever, we do not deal with it, and hopefully we will report on
it some other time.
31
in the database are grouped together according
to their grammatical behavior, and we will refer
to each of the groups as a lexical type in the
rest of the paper. A typical lexical item con-
sists of an identifier, and then a triple consist-
ing of the orthography, lexical-type and predicate:
e.g., inu n 1 = 〈 “ :”, common-noun-lex,
dog n animal〉. The grammar treats all mem-
bers of the same lexical type in the same way. the
lexical type is the locus of syntactic and structural
semantic information. Examples of lexical types
will be described in §2.
The database could also benefit a wide range
of language researchers, not just those who are
treebanking. As the development of the treebank
and grammar proceeds, together they describe the
language (Japanese in this study) with increasing
accuracy. As a result, the database that we ob-
tain from the sophisticated treebank and grammar
can be thought of as showing us the real view of
the Japanese lexicon. Thus, though many of the
details of the treebank and grammar are frame-
work dependent, the database will provide NLP
researchers who are aiming at deep linguistic pro-
cessing of Japanese with a basic and reliable ref-
erence Japanese lexicon. The correctness can be
verified by examining the treebanked examples.
Such a resource is useful for Japanese language
teachers, lexicographers, and linguists, in addi-
tion to NLP researchers.
The next section describes the framework of
treebanking and motivates the need for the lexical
type database. The third section discusses what
information the lexical type database should con-
tain to facilitate treebanking and grammar devel-
opment; illustrates the contents of the database;
and shows how the database is created. The fourth
section discusses the usefulness of the lexical type
database for many purposes other than treebank-
ing. An overview of related works follows in the
fifth section. Finally, we conclude the paper with
a discussion of our plans for future work.
2 Background to the Database
The treebanking process is illustrated in Fig-
ure 1. As the figure shows, our treebank is
semi-automatically generated by a computational
grammar (and a parser). Each sentence is parsed
and the intended reading chosen from the possi-
Development (refinement)
GRAMMAR
Treebanking (manual annotation)
TREEBANK
automatic parsingfeedback
Figure 1: Treebanking Cycles
ble interpretations. In doing so, we find the gram-
mar’s flaws such as insufficient coverage and spu-
rious ambiguities. The feedback allows us to re-
fine the grammar so that it can have wider cov-
erage and be more appropriately restricted. Cur-
rently this process is carried out by several people,
distributed over four continents.
Although most treebanks are rarely updated,
we consider the updating an integral part of the
process. Thus our treebank is dynamic in the
sense of Oepen et al. (2004).
As is often the case with detailed linguistic
treebanking, our grammar and treebank consist
of very fine-grained linguistic information. For
example, our grammar, hence our treebank, dis-
tinguishes several usages of the Japanese dative
marker ni. The Japanese sentence (1) can repre-
sent the two meanings described in (1a) and (1b).
Lexical type names for each usage of ni are writ-
ten in typewriter font.3
(1) hanasiai-wa
discussion-TOP
sinya-ni
midnight-DAT
itaru
reach
a. “The discussion comes (to a conclusion)
at midnight.”
ni as adv-p-lex-1
b. “The discussion continues until mid-
night.”
ni as ga-wo-ni-p-lex
The dative phrase, sinya-ni (midnight-DAT), can
act as either an adjunct (1a)4 or an object of itaru
“reach” (1b). Below is an example showing other
usages of ni.
(2) Ken-wa
-TOP
yuka-o
floor-ACC
kirei-ni
clean-DAT
migaku
polish
3These are actual names of the lexical types implemented
in our grammar and might not be understandable to people
in general.
4The object, a conclusion, is expressed by a phonolog-
ically null pronoun. This is the so-called “pro-drop” phe-
nomenon.
32
a. “Ken polishes a floor clean.”
(The floor is clean.)
ni as naadj2adv-end-lex
b. “Ken cleanly polishes a floor.”
(His way of polishing the floor is clean.)
ni as adv-p-lex-6
The dative phrase, kirei-ni (clean-DAT), is used as
an adjunct in both (2a) and (2b), but their usages
and meanings are different. The usage in (2b) is
an ordinary adverb that describes the manner of
Ken’s polishing the floor as clean, while in (2a)
the dative phrase describes the resulting situation
of the floor after polishing as clean. In addition,
the nis in (1) and (2) are different in that the for-
mer takes nouns as its complement while the lat-
ter takes adjectives. Thus, the four usages in (1a),
(1b), (2a) and (2b) must be distinguished so that
we can obtain correct syntactic structures and se-
mantic representations. In our terms, these nis are
said to belong to different lexical types.5 Simi-
larly, our grammar distinguishes usages of other
words, notably functional ones.
However, as we augment the grammar with
finer distinctions, the grammar becomes more and
more opaque and difficult to maintain, and so is
the treebank. This is problematic in three ways.
Firstly, when we annotate parser outputs of one
sentence, we have to see which parse is correct
for the sentence. Consequently, we have to distin-
guish which word usage is correct for each word
in the sentence. However, this task is not always
trivial, since our grammar’s word usage distinc-
tion is very fine grained as shown above. Sec-
ondly, when we add a word to the grammar to
get wider coverage, we have to see which lexical
type the word belongs to. That is, we are required
to be familiar with lexical types of the grammar.
Thirdly, in collaborative grammar development, it
sometimes happens that a developer accidentally
introduces a new lexical type that represents over-
lapping functionality with an existing type. This
causes spurious ambiguity. As a result, the gram-
mar will be unnecessarily bloated, and the tree-
bank will also be easily inconsistent. Again, we
see that comprehensive knowledge of the gram-
mar’s lexical types is indispensable.
5Usages of the Japanese dative marker, ni, are extensively
discussed in, for example, Sadakane and Koizumi (1995).
In summary, it is important to make clear (i)
what lexical types are assumed in a grammar and
a treebank and (ii) how differently they are used
from each other, so that we can make the treebank
annotation and grammar development consistent.
Our solution to the problem is to construct a
lexical type database of a treebank and a gram-
mar. The database is expected to give us explicit
information on (i) what lexical types are imple-
mented in the grammar and are used in the tree-
bank and (ii) how a word is used in Japanese and
is distinguished from other words.
3 Architecture of the Database
This section details the content of the
database and the method of its construc-
tion. The database itself is on-line at
http://pc1.ku-ntt-unet.ocn.ne.
jp/tomcat/lextypeDB/.
3.1 Content of the Database
First of all, what information should be included
in such a database to help treebank annotators and
grammar developers to work consistently? Ob-
viously, once we construct an electronic lexicon,
whatever information it includes, we can easily
see what lexical types are assumed in the gram-
mar and treebank. But we have to carefully con-
sider what to include in the database to make it
clear how each of the lexical types are used and
distinguished.
We include five kinds of information:
(3) Contents of the Database
a. Linguistic discussion
i Name
ii Definition
iii Criteria to judge a word as belong-
ing to a given lexical type
iv Reference to relevant literature
b. Exemplification
i Words that appear in a treebank
ii Sentences in a treebank that contain
the words
c. Implementation
i The portion of grammar source file
that corresponds to the usage
33
ii Comments related to the portion
iii TODOs
d. Links to “confusing” lexical types
e. Links to other dictionaries
That is, we describe each lexical type in
depth (3a–3c) and present users (treebank an-
notators and grammar developers) explicit links
to other lexical types that share homony-
mous words (3d) (e.g. adv-p-lex-1 vs
ga-wo-ni-case-p-lex in (1)) to make it
clear what distinguishes between them. Further,
we present correspondences to other computa-
tional dictionaries (3e).
Linguistic discussion To understand lexical
types precisely, linguistic observations and anal-
yses are a basic source of information.
Firstly, the requirements for naming lexical-
types in a computational system (3ai) are that
they be short (so that they can be displayed in
large trees) and easily distinguishable. Type
names are not necessarily understandable for any-
one but the developers, so it is useful to link
them to more conventional names. For example
ga-wo-ni-p-lex is a Case Particle (p
%.V).
Next, the definition field (3aii) contains a
widely accepted definition statement of the lexi-
cal type. For example, ga-wo-ni-p-lex (1b)
can be defined as “a particle that indicates that a
noun it attaches to functions as an argument of a
predicate.” Users can grasp the main characteris-
tics from this.
Thirdly, the criteria field (3aiii) provides users
with means of investigating whether a given word
belongs to the class. That is, it provides posi-
tive and negative usage examples. By such us-
age examples, developers can easily find dif-
ferences among lexical types. For example,
adv-p-lex-1 (1a) subcategorizes for nouns,
while adv-p-lex-6 (2b) subcategorizes for
adjectives. Sentences like (1a) and (2b) that fit
such criteria should also be treebanked so that
they can be used to test that the grammar covers
what it claims. This is especially important for
regression testing after new development.
Finally, the reference field (3aiv) points to rep-
resentative papers or books dealing with the lex-
ical type. This allows the grammar developers to
quickly check against existing analyses, and al-
lows users as well to find more information.
Exemplification Examples help users under-
stand lexical types concretely. As we have con-
structed a treebank that is annotated with linguis-
tic information, we can automatically extract rele-
vant examples exhaustively. We give the database
two kinds of examples: words, that are instances
of the lexical types (3bi), and sentences, tree-
banked examples that contain the words (3bii).
This link to the linguistically annotated corpus
examples helps treebankers to check for consis-
tency, and grammar developers to check that the
lexical types are grounded in the corpus data.
Implementation Grammar developers need to
know the actual implementation of lexical types
(3ci). Comments about the implementation (3cii)
are also helpful to ascertain the current status.
Although this section is necessarily framework-
dependent information, all project groups that are
constructing detailed linguistic treebanks need to
document this kind of information. We take our
examples from JACY (Siegel and Bender, 2002),
a large grammar of Japanese built in the HPSG
framework. As actual implementations are gen-
erally incomplete, we use this resource to store
notes about what remains to be done. TODOs
(3ciii) should be explicitly stated to inform gram-
mar developers of what they have to do next.
We currently show the actual TDL defini-
tion, its parent type or types, category of
the head (SYNSEM.LOCAL.CAT.HEAD), valency
(SYNSEM.LOCAL.CAT.VAL), and the semantic
type (SYNSEM.LOCAL.CONT).
Links to “confusing” lexical types For users to
distinguish phonologically identical but syntacti-
cally or semantically distinct words, it is impor-
tant to link confusing lexical types to one another
within the database. For example, the four lexical
types in (1) and (2) are connected with each other
in terms of ni. That way, users can compare those
words in detail and make a reliable decision when
trying to disambiguate usage examples.6
6Note that this information is not explicitly stored in
the database. Rather, it is dynamically compiled from the
database together with a lexicon database, one of the com-
ponent databases explained below, when triggered by a user
query. User queries are words like ni.
34
Links to other dictionaries This information
helps us to compare our grammar’s treatment
with that of other dictionaries. This compar-
ison would then facilitate understanding of
lexical types and extension of the lexicon. We
currently link lexical types of our grammar
to those of ChaSen (Matsumoto et al., 2000),
Juman (Kurohashi and Nagao, 2003), ALT-
J/E (Ikehara et al., 1991) and EDICT (Breen,
2004). For example, ga-wo-ni-case-p-lex
is linked to ChaSen’s
%.V-p
%.V-	�)x
(particle-case particle-general),
Juman’sp
%.V(case particle), and
ALT-J/E’s
��.�-p
%.V-,.Vn
%.V��
(adjunct-case particle-noun/par-
ticle suffix).
Figure 2 shows the document generated from
the lexical type database that describes the lexical
type, ga-wo-ni-p-lex.
3.2 Method of Database Construction
The next question is how to construct such a
database. Needless to say, fully manual construc-
tion of the database is not realistic, since there
are about 300 lexical types and more than 30,000
words in our grammar. In addition, we assume
that we will refer to the database each time we
annotate parser outputs to build the treebank and
that we develop the grammar based on the tree-
banking result. Thus the database construction
process must be quick enough not to delay the
treebanking and grammar development cycles.
To meet the requirement, our method of con-
struction for the lexical type database is semi-
automatic; most of the database content is con-
structed automatically, while the rest must be en-
tered manually. This is depicted in Figure 3.
• Content that is constructed automatically
– Lexical Type ID (Grammar DB)
– Exemplification (3b) (Treebank DB)
– Implementation (3ci,ii) (Grammar DB)
– Link to “confusing” lexical types (3d)
(Lexicon DB)
– Link to Other Lexicons (3e) (OtherLex
DB)
• Content that is constructed manually
– Linguistic discussion (3a)
– TODOs (3ciii)
3.2.1 Component Databases
To understand the construction process, de-
scription of the four databases that feed the lex-
ical type database is in order. These are the gram-
mar database, the treebank database, the lexicon
database, and the OtherLex database.
• The grammar database contains the actual
implementation of the grammar, written as
typed feature structures using TDL (Krieger
and Schafer, 1994). Although it contains the
whole implementation (lexical types, phrasal
types, types for principles and so on), only
lexical types are relevant to our task.
• The lexicon database gives us mappings be-
tween words in the grammar, their orthogra-
phy, and their lexical types. Thus we can see
what words belong to a given lexical type.
The data could be stored as TDL, but we
use the Postgresql lexdb (Copestake et al.,
2004), which simplifies access.
• The treebank database stores all treebank in-
formation, including syntactic derivations,
words, and the lexical type for each word.
The main treebank is stored as structured
text using the [incr tsdb()] (Oepen et al.,
2002). We have also exported the deriva-
tion trees for the treebanked sentences into
an SQL database for easy access. The leaves
of the parse data consist of words, and their
lexicon IDs, stored with the ID of the sen-
tence in which the word appears.
• We also use databases from other sources,
such as ChaSen, Juman and Edict.
3.2.2 Automatic Construction
Next we move on to describe the automatic
construction. Firstly, we collect all lexical types
assumed in the grammar and treebank from the
grammar database. Each type constitutes the ID
of a record of the lexical type database.
Secondly, we extract words that are judged to
belong to a given lexical type and sentences that
contains the words (Example (3b)) from the tree-
bank database compiled from the Hinoki tree-
bank (Bond et al., 2004a). The parsed sentences
35
p
%.V, ga-wo-ni-p-lex (�,�,
)
Linguistic Discussion
ga-wo-ni-p-lex particles attach to a noun and indicate what grammatical relation (e.g., subject or object)
the noun takes on in relation to a predicate. It does not mean anything by itself.
Right Wrong
�����
'���
@����
����
6����-���10(��"���
Literature
[1] Koichi Takezawa. A Configurational Approach to Case Marking in Japanese. Ph.D. dissertation,
University of Washington, 1987.
[ bib ]
[2] Shigeru Miyagawa. Structure and Case Marking in Japanese (Syntax and Semantics 22). Academic
Press, 1989.
[ bib ]
Examples
Lexical Entries (6)
�(ga),�(ni-case),
(o)
Example Sentences (54280)
Examples for�(ga)

q� ������2=�u���������A���)D���0�'�����A��
��f����
.%�����
/�5�
/�)��$�����
�Ip�����

q�$�
G����
��
d1b�4P
Examples for�(ni-case)

0�
����$�����'�
6<��
���
-)��6<��
���
Examples for
(o)

q
�
 :
�
����
!�H
\���
More Examples
TDL Summary
TDL Definition
ga-wo-ni-p-lex := case-p-lex & 
                [SYNSEM.LOCAL.CAT.VAL.COMPS.FIRST.LOCAL.CAT.HEAD noun_head].
Supertype Head Category Valency Content
case-p-lex overt-case-p_head p_sat mrs
TODO
Dative subjects of stative predicates are not recognized.
"������
���"
See also mental-stem-lex.
Links
CHASEN’s Lexical type JUMAN’s Lexical type ALT-J/E’s Lexical type

%.V-p
%.V-	�)xp
%.V
��.�-p
%.V-,.Vn
%.V��
Lexical Type List
Figure 2: Screenshot of the lexical type ga-wo-ni-p-lex
36
Manual
Input
Grammar DB
- Lexical Type ID
- Source
OtherLex DB
- Other Lex ID
- Other Lex Type
- Orthography
Lexical Type DB
- Lexical Type ID
- Linguistic Discussion
- Exemplification
- Implementation
- TODOs
- Other Lexicons
Lexicon DB
- Lexicon ID
- Orthography
- Lexical Type ID
Treebank DB
- Lexicon ID
- Orthography
- Sentence ID
User:
-)
OtherLex
Interface
Query
“Confusing”
Links
Figure 3: The Lexical Type Database Construction
can be seen in various forms: plain text, phrase
structure trees, derivation trees, and minimal re-
cursion semantics representations. We use com-
ponents from the Heart-of-Gold middleware to
present these as HTML (Callmeier et al., 2004).
Thirdly, implementation information except for
TODOs is extracted from the grammar database
(3ci,ii).
Fourthly, in order to establish “confusing” lex-
ical type links (3d), we collect from the lexicon
database homonyms of a word that users enter as
a query. To be more precise, the lexicon database
presents all the words with the same orthogra-
phy as the query but belonging to different lexical
types. These lexical types are then linked to each
other as “confusing” in terms of the query word.
Fifthly, we construct links between our lexical
types and POS’s of other lexicons such as ChaSen
from OtherLex DB (3e). To do this, we prepare
an interface (a mapping table) between our lexi-
cal type system and the other lexicon’s POS sys-
tem. As this is a finite mapping it could be made
manually, but we semi-automate its construction.
The similarity between types in the two databases
(JACY and some other lexicon ) is calculated as
the Dice coefficient, where W(LA) is the number
of words W in lexical type L:
sim(LA,LB) = 2×|(W(LA ∩LB)||W(L
A)|+|W(LB)|
(1)
The Dice coefficient was chosen because of its
generality and ease of calculation. Any pair
where sim(LA,LB) is above a threshold should
potentially be mapped. The threshold must be set
low, as the granularity of different systems can
vary widely.
3.2.3 Manual Construction
Linguistic discussion (3a) and implementation
TODOs (3ciii) have to be entered manually. Lin-
guistic discussion is especially difficult to collect
exhaustively since the task requires an extensive
background in linguistics. We have several lin-
guists in our group, and our achievements in this
task owe much to them. We plan to make the in-
terface open, and encourage the participation of
anyone interested in the task.
The on-line documentation is designed to com-
plement the full grammar documentation (Siegel,
2004). The grammar documentation gives a top
down view of the grammar, giving the overall mo-
tivation for the analyses. The lexical-type docu-
mentation gives bottom up documentation. It can
easily be updated along with the grammar.
Writing implementation TODOs also requires
expertise in grammar development and linguis-
tic background. But grammar developers usually
take notes on what remains to be done for each
lexical type anyway, so this is a relatively simple
task.
After the database is first constructed, how is
it put to use and updated in the treebanking cy-
cles described in Figure 1? Figure 4 illustrates
this. Each time the grammar is revised based on
treebank annotation feedback, grammar develop-
ers consult the database to see the current status
of the grammar. After finishing the revision, the
grammar and lexicon DBs are updated, as are the
corresponding fields of the lexical type database.
Each time the treebank is annotated, annotators
can consult the database to make sure the chosen
parse is correct. Following annotation, the tree-
bank DB is updated, and so is the lexical type
database. In parallel to this, collaborators who are
37
Development (refinement)
GRAMMAR
Treebanking (manual annotation)
TREEBANK
automatic
parsingfeedback
LEXICAL TYPE
DATABASE
WWW
Reference
Updating Grammar and Lexicon DBs
Reference
Updating Treebank DB
Linguistic Discussion
Figure 4: Database Construction Intergrated with Treebanking Cycles
ChaSenJuman ALT-J/E
The Lexical Type
Database
EDICT LexicalResource2Lexical
Resource1
ChaSen
InterfaceJumanInterface ALT-J/EInterface
EDICT
Interface Interface2Interface1
Figure 5: Synthesis of Lexical Resources
familiar with linguistics continue to enter relevant
linguistic discussions via the WWW.
4 Lexical Type Database as a General
Linguistic Resource
In this section, we discuss some of the ways the
database can benefit people other than treebank
annotators and grammar developers.
One way is by serving as a link to other lexi-
cal resources. As mentioned in the previous sec-
tion, our database includes links to ChaSen, Ju-
man, ALT-J/E, and EDICT. Currently, in Japanese
NLP (and more generally), various lexical re-
sources have been developed, but their intercor-
respondences are not always clear. These lexical
resources often play complementary roles, so syn-
thesizing them seamlessly will make a Japanese
lexicon with the widest and deepest knowledge
ever. Among our plans is to realize this by
means of the lexical type database. Consider Fig-
ure 5. Assuming that most lexical resources con-
tain lexical type information, no matter how fine
or coarse grained it is, it is natural to think that
the lexical type database can act as a “hub” that
links those lexical resources together. This will
be achieved by preparing interfaces between the
lexical type database and each of the lexical re-
sources. Clearly, this is an intelligent way to syn-
thesize lexical resources. Otherwise, we have to
prepare nC2 interfaces to synthesize n resources.
The problem is that construction of such an inter-
face is time consuming. We need to further test
generic ways to do this, such as with similarity
scores, though we will not go on further with this
issue in this paper.
Apart from NLP, how can the database be used?
In the short term our database is intended to pro-
vide annotators and grammar developers with a
clear picture of the current status of the treebank
and the grammar. In the long term, we expect to
create successively better approximations of the
Japanese language, as long as our deep linguistic
broad coverage grammar describes Japanese syn-
tax and semantics precisely. Consequently, the
database would be of use to anyone who needs an
accurate description of Japanese. Japanese lan-
guage teachers can use its detailed descriptions
of word usages, the links to other words, and the
real examples from the treebank to show for stu-
dents subtle differences among words that look
the same but are grammatically different. Lexi-
cographers can take advantage of its comprehen-
siveness and the real examples to compile a dic-
tionary that contains full linguistic explanations.
The confidence in the linguistic descriptions is
based on the combination of the precise grammar
linked to the detailed treebank. Each improves the
other through the treebank annotation and gram-
mar development cycle as depicted in Figure 1.
5 Related Work
Tsuchiya et al. (2005) have been constructing a
database that summarizes multiword functional
38
expressions in Japanese. That describes each
expression’s linguistic behavior, usage and ex-
amples in depth. Notable differences between
their database and ours are that their database is
mostly constructed manually while ours is con-
structed semi-automatically, and that they target
only functional expressions while we deal with all
kinds of lexical types.
Hypertextual Grammar development (Dini and
Mazzini, 1997) attempted a similar task, but fo-
cused on documenting the grammar, not on link-
ing it to a dynamic treebank. They suggested cre-
ating the documentation in the same file along
with the grammar, in the style of literate program-
ming. This is an attractive approach, especially
for grammars that change constantly. However,
we prefer the flexibility of combining different
knowledge sources (the grammar, treebank and
linguistic description, in addition to external re-
sources).
The Montage project (Bender et al., 2004) aims
to develop a suite of software whose primary au-
dience is field linguists working on underdocu-
mented languages. Among their tasks is to fa-
cilitate traditional grammatical description from
annotated texts by means of one of their products,
the Grammar export tool. Although in the paper
there is little explicit detail about what the “tradi-
tional grammatical description” is, they seem to
share a similar goal with us: in the case of Mon-
tage, making grammatical knowledge assumed in
underdocumented languages explicit, while in our
case making lexical types assumed in the treebank
and the computational grammar understandable
to humans. Also, some tools they use are used
in our project as well. Consequently, their pro-
cess of grammatical description and documenta-
tion looks quite similar to ours. The difference
is that their target is underdocumented languages
whose grammatical knowledge has so far not been
made clear enough, while we target a familiar
language, Japanese, that is well understood but
whose computational implementation is so large
and complex as to be difficult to fully compre-
hend.
Another notable related work is the COMLEX
syntax project (Macleod et al., 1994). Their goal
is to create a moderately-broad-coverage lexicon
recording the syntactic features of English words
for purposes of computational language analysis.
They employed elves (“elf” = enterer of lexical
features) to create such a lexicon by hand. Natu-
rally, the manual input task is error-prone. Thus
they needed to prepare a document that describes
word usages by which they intended to reduce
elves’ errors. It is evident that the document
plays a role similar to our lexical type database,
but there are important divergences between the
two. First, while their document seems to be con-
structed manually (words chosen as examples of
lexical types in the documentation are not always
in the lexicon!), the construction process of our
database is semi-automated. Second, somewhat
relatedly, our database is electronically accessible
and well-structured. Thus it allows more flexi-
ble queries than a simple document. Third, unlike
COMLEX, all the lexical types in the database
are actually derived from the working Japanese
grammar with which we are building the tree-
bank. That is, all the lexical types are defined
formally. Fourth, examples in our database are all
real ones in that they actually appear in the tree-
bank, while most of the COMLEX examples were
created specifically for the project. Finally, we are
dealing with all kinds of lexical types that appear
in the treebank, but the COMLEX project targets
only nouns, adjectives, and verbs.
6 Future Work
We are currently experimenting with moving
some of the information (in particular the type
name and criteria) into the actual grammar files,
in the same way as Dini and Mazzini (1997). This
would make it easier to keep the information in
sync with the actual grammar.
We have discussed the motivation, contents and
construction of the lexical type database. We plan
to evaluate the database (i) by measuring tree-
bank inter-annotator agreement and (ii) by evalu-
ating the coverage, the amount of spurious ambi-
guity, and efficiency of the grammar before and
after introducing the database in the treebank-
ing and grammar development cycles. We ex-
pect that treebank annotators will be more con-
sistent when they can refer to the database and
that grammar developers can more easily find the
grammar’s flaws (like lack of lexical items and
overlapping implementations of the same lexical
39
type) by looking into the database.
Although this paper deals with a lexical type
database of Japanese, the importance of such a
database certainly holds for any large scale deep
grammar. We use the tools from the DELPH-
IN collaboration7 and plan to make our addi-
tions available for groups working with other lan-
guages. In particular, we plan to construct a lex-
ical type database for the Redwoods treebank,
which is semi-automatically constructed from the
English Resource Grammar (ERG) (Flickinger,
2000).
Acknowledgements
We would like to thank the other members
of Machine Translation Research Group, Dan
Flickinger, Stephen Oepen, and Jason Katz-
Brown for their stimulating discussion.
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