Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 161–170,
Portland, Oregon, June 19-24, 2011.
c
2011 Association for Computational Linguistics
A Comprehensive Dictionary of Multiword Expressions

Kosho Shudo
1
, Akira Kurahone
2
, and

Toshifumi Tanabe
1

1
Fukuoka University, Nanakuma, Jonan-ku, Fukuoka, 814-0180, JAPAN
{shudo,tanabe}@fukuoka-u.ac.jp
2
TechTran Ltd., Ikebukuro, Naka-ku, Yokohama, 231-0834, JAPAN Abstract
It has been widely recognized that one of the
most difficult and intriguing problems in
natural language processing (NLP) is how to
cope with idiosyncratic multiword expressions.
This paper presents an overview of the

without any human insight. Recognizing the
crucial importance of such expressions, one of the
authors of the current paper began in the 1970s to
construct a Japanese electronic dictionary with
comprehensive inclusion of idioms, idiom-like
expressions, and probabilistically idiosyncratic
expressions for general use. In this paper, we begin
with an overview of the JDMWE (Japanese
Dictionary of Multi-Word Expressions). It has
approximately 104,000 dictionary entries and
covers potentially at least 750,000 expressions.
The most important features of the JDMWE are:
1. A large notational, syntactic, and semantic
diversity of contained expressions
2. A detailed description of syntactic function and
structure for each entry expression
3. An indication of the syntactic flexibility of entry
expressions (i.e., possibility of internal
modification of constituent words) of entry
expressions.
In section 2, we outline the main features of the
present study, first presenting a brief summary of
significant previous work on this topic. In section 3,
we propose and describe the criteria for selecting
MWEs and introduce a number of classes of
multiword expressions. In section 4, we outline the
format and contents of the JDMWE, discussing the
information on notational variants, syntactic
functions, syntactic structures, and the syntactic
flexibility of MWEs. In section 5, we describe and

constructed a database of 13,000 English idioms
tagged with syntactic structures; Villavicencio
(2004) attempted to compile lexicons of English
idioms and verb-particle constructions (VPCs) by
augmenting existing single-word dictionaries with
specific tables; Baptista et al. (2004) reported on a
dictionary of 3,500 Portuguese verbal MWEs with
ten syntactic structures; Fellbaum et al. (2006)
reported corpus-based studies in developing
German verb phrase idiom resources; and recently,
Laporte et al. (2008) have reported on a dictionary
of 6,800 French adverbial MWEs annotated with
15 syntactic structures.
Our JDMWE approach differs from these
studies in that it can treat more comprehensive
types of MWEs. Our system can handle almost all
types of MWEs except compositional compounds,
named entities, acronyms, blends, politeness
expressions, and functional expressions; in contrast,
the types of MWEs that most of the other studies
can deal with are limited to verb-object idioms,
VPCs, verbal MWEs, support-verb constructions
(SVCs) and so forth.
Many attempts have been made to extract
MWEs automatically using statistical corpus-based
methods. For example, Pantel et al. (2001) sought
to extract Chinese compounds using mutual
information and the log-likelihood measure. Fazly
et al. (2006) attempted to extract English verb-
object type idioms by recognizing their structural

(1980) compiled a lexicon of 3,500 functional
multiword expressions and used the lexicon for a
morphological analysis of Japanese. Koyama et al.
(1998) made a seven-point increase in the
precision rate of kana-to-kanji conversion for a
commercial Japanese word processor by using a
prototype of the JDMWE with 65,000 MWEs.
Baldwin et al. (2003) discussed the treatment of
Japanese MWEs in the framework of Sag et al.
(2002). Shudo et al. (2004) pointed out the
importance of the auxiliary-verbal MWEs and their
non-propositional meanings (i.e., modality in a
generalized sense). Hashimoto et al. (2009)
studied a disambiguation method of semantically
ambiguous idioms using 146 basic idioms.
3 MWEs Selected for the JDMWE
The human deliberate judgment is indispensable
for the correct, extensive extraction of MWEs. In
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