Semi-automatic Refinement ofthe JMdict/EDICT Japanese-English Dictionary
Francis Bond∗ and Jim Breen∗∗
NICT Computational Linguistics Group∗∗
Monash University<bond@ieee.org,jim.breen@infotech.monash.edu.au>∗Abstract
The JMdict/EDICT Japanese-English Dictionary is a freely-available dictionary distributedin XML (JMdict)and text (EDICT) formats. It is widely used as a source of lexical material in
dictionary systems and text-processing projects. We propose two refinements to make the dictionary
more computationally tractable: marking entries where the English is not a translation equivalentand expanding contracted entries. We then propose and apply semi-automatic methods to refine
existing entries. The resulting dictionary is shown to be more suitable for the construction ofmachine translation rules.

1Introduction

Resources built for one task can often be useful in others. WordNet, for example, started
off as an test-bed for a particular model of lexical organization (Fellbaum, 1998, p4) and is
now widely used in natural language processing(NLP) applications. In this paper we look at theJapanese/English lexicon JMdict/EDICT (Breen,2004), which started out as a voluntary project toproduce a freely available Japanese/English Dictionary in machine-readable form. In addition
to being useful for people as a bilingual dictionary, it is also widely used in NLP applications.
For example, it has been the base to make compound noun lexicons (Tanaka and Matsuo, 1999;
Ohmori and Higashida, 1999), new bilingual lexicons (Paik et al., 2001; Apel, 2002; Sjöbergh, 2005;
Zhang et al., 2005; Fujita and Bond, 2006; Bondand Ogura, 2007) and machine translation transfer rules Bond et al. (2005).
We look at two ways of making the dictionary
even more tractable for NLP tasks: (1) marking entries where the English is not a translationequivalent and (2) expanding contracted entries.
Finally, we discuss some planned future enhancements.

2JMdict/EDICTThe JMdict/EDICT project now has approximately 110,000 Japanese/English entries
recorded, with the number increasing at about
1,000 per month. A WWW-based system for submitting amendment and new entry suggestions isyielding about 100 submissions each day, whichclose to the limit that can be handled by the soleeditor (Breen).
The project dictionary is distributed in threeformats:
1. the full JMdict (XML) format, bothinJapanese-EnglishandJapaneseEnglish/German/French/etc. versions.
2. the original EDICT format, which only allows for one kanji word and one reading per
entry. Thus JMdict entries which have alternative kanji or okurigana forms, or whichhave alternative readings will result in multiple entries in the EDICT file.
3. the EDICT2 format (shown below) which allows for multiple kanji words and readings in
an entry, and is in effect a human-readable
equivalent of the JMdict entries.

The dictionary files are generated daily, andare available via ftp and rsync, thus allowing
WWW servers that use the files to stay up-todate.1
At present all editing is taking place at Monash
University, with semi-automated creation of newentries, and manual amendment of existing entries. A new WWW-based maintenance system innearing completion which will enable distributed
editing with a pool of editors. The new system
has a more flexible database which will allow additional information to be included in the entries,and greater access to the data by project members.

3Enhancements to the Dictionary Structure(3)paddy/In this case, two translations have been collapsed
into the second gloss: rice field and rice paddy.
This contraction of entries is important in paper
dictionaries, where space is precious, but causes
problems for electronic access: the translationequivalent rice field will not be an exact matchand the translation rice paddy is not even a contiguous string.
The solution to the first problem is to explicitly mark the type of each gloss. The default typeis equ (translation equivalent) whereas explanations are marked as exp. Simplified examples ofthis marked up in xml are shown in (4):(4)

A typical entry (for jiten “dictionary”) in theEDICT2 format is shown below:(1)öLW ?l[] (1) /(n,n-suf) spot/mark/(2) point/dot/(3) (n-suf) counter for goods or items/(P)/In this case the third gloss is not a translation equivalent, but rather an explanation: ten“piece” is used as a suffix for numbers when counting goods or items. We would not expect to want
to look up this directly, and could not directly use
the gloss to create translation rules.
Another example where the reverse look up
fails is disjunctive entries such as (2):
1W ?l[] ...
<gloss g type="equ" >spot</gloss><gloss g type="exp">counter for goods
or items</gloss>

solution to the second is even simpler:öL(P);-L(oK) [1?l] /(n) dictionary/(P)/ splitThethe entry with “or” into two separate entries:

The various marks indicate that it is a common
word (P), there is an orthographic variant with
old Kanji (oK), and that the Japanese part ofspeech is a noun (n). In this entry, as in mostentries, the English gloss is a translation equivalent and the entry is effectively reversible: h↔ dictionaryi. This allows the use of JMdict
as an English→Japanese lexicon, even though it
basically Japanese→English.
However there are some entries where the reversibility does not hold. For example consider
the simplified entry for ten “piece” (2):(2)[ª[ l:; l1] /(n) farmland/rice field or

http://ftp.monash.edu.au/pub/nihongo/00INDEX.html(5)4[ª l: l1] /(n) farmland/ rice field/[;rice paddy/

Expandingtries

DisjunctiveEn-There were 2843 entries containing ” or ” in JMdict (1.3% of the Japanese-English entries contained a disjunctive gloss). Four word entries werethe most common, with the longest entry consisting of 35 words.
An initial survey of the glosses found three major types (G) good translation equivalents (D) disjunctive glosses and (E) explanations.

h$¤$[5$K$] /right or wrong/4Þ[Y] /yes or no/D j½[,(.-] /(n) an international or universal language/℄ó[.D^] /(n) annual revenue or income/E ¾Xd[aX℄Od] /(v5r,vt) to read throughG(difficult passage or particularly long book)/

The 1,271 E entries included 304 that wereThe vast majority were of short entries (5words or less) were of type D, while the longer actually disjunctive, but the vast majority wereexplanations such as (9), which was corrected toentries were mainly of type E.
(10)
The algorithm for rewriting was simple:(9)

1. Remove any articles from the gloss
2. if the final word is two, other or another⇒G
3. elsif the Japanese entry ends in⇒G(10)>0Ü >0µ #030;[] /(n) {food} sushi riceand other ingredients pressed in box or mould(mold)/> 0 Ü;> 0  µ[# 0 3 0] /(n) {food} os-hizushi/ (expl.) sushi rice and other ingredients pressed in box or mould/$

We also investigated testing the validity ofsplitting the disjunctive entries by looking them
4. elsif the gloss appear more than 3 times (e.gup in a different lexicon (EDR, 1990), but found
yes or no)
only 35 hits, too few to be useful. Similarly, we in⇒Gvestigated looking up entries in a bilingual alignedcorpus (Utiyama and Takahashi, 2003), but found
5. elsif there 6 or more words
too few hits to be useful.
⇒E
6. elsif or is the second word (w2 )D split into w1 , w4 , w5 , . . . and w3 , w4 , w5 , . . .

6

Future Enhancements

A number of enhancements are under consider7. elsif or is the second last word (wn−2 )D split into . . . wn−4 , wn−3 , wn−2 and ation and at various stages of implementation.
They include:. . . wn−4 1, wn−3 , wn
8. else leave to be hand-checked
As a result of this 72 entries were judged to begood (G), 1,500 to be disjunctive (D) and the remaining 1,271 to be explanations (E) or requiring
further checking.
In the examples given above, the disjunctive
entries are rewritten as follows:(6)(7)

j½ ,(.-[] /(n) international language/
universal language/

I [H()] /(n,vs) poor reception/ inhospitable reception/

5Evaluation

The effectiveness of splitting was tested by manualevaluation. For those judged G, there were only
three errors, all caused by errors in the originalentries.
For those split (D), only 2% were erroneous.
The main source of errors was splitting good entries, such as (8).
(8)¬ [&`1=] /(n) truth or falsehood/

1. Extension of frequency-of-use information.
At present about 30% of entries have frequency ranking information based primarily on a newspaper-based ranked word-list.
This is being extended and refined using
WWW-based word-frequency metrics.
2. Expansion of orthographical variants, including okurigana variants and kana substitution for non-Joujou kanji. Experimentation is under way into the automatic generation of potential variants combined withtesting possible variants against the WWW
and other corpora to determine their validity
and level of usage.
3. Greater delineation of senses. At present
only about 5% of entries have senses marked
in the English glosses. While Japanese is notregarded as being highly polysemous, there
is considerable scope to improve the levelof sense tagging. There is some potential
to employ NLP techniques on the English
glosses to identify candidates for sense delineation.

4. Extension of cross-referencing including in- Francis Bond and Kentaro Ogura. 2007. Combining
linguistic resources to create a machine-tractabledication of synonyms and antonyms. At
Japanese-Malay dictionary. Language Resources andpresent there is a relatively low level ofEvaluation. (Special issue on Asian language techcross-referencing. Some experimentation usnology).
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dictionary. In Coling 2004 Workshop on Multilingual
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Linguistic Resources, pages 71–78. Geneva.

5. Marking of domains. At present there is limited domain marking in a number of entries, EDR. 1990. Concept dictionary. Technical report,
Japan Electronic Dictionary Research Institute, Ltd.
and it is highly desirable that this be extended. An issue is determining an appro- Christine Fellbaum, editor. 1998. WordNet: An Elecpriate set of word domains to use. A postronic Lexical Database. MIT Press.
sibility being explored is the application ofSanae Fujita and Francis Bond. 2006. A method ofWordnet synsets.
creating new valency entries. Machine Translation.

(to appear).
6. Adding verb translations for verbal nouns() verbs. At present EDICT generCatherine Macleod, Ralph Grishman, Adam Meyers,
ally records only the noun translation: for
Leslie Barrett, and Ruth Reeves. 1998. NOMLEX:
example:[] /(n,vs) inspection/.
A lexicon of nominalizations. In Proceedings of EUWe would like to expand the entry to:
RALEX’98. Liege, Belgium.
[] /(n) inspection/ (vs) inspect.
This can be done semi-automatically and Kumiko Ohmori and Masanobu Higashida. 1999. Extracting bilingual collocations from non-aligned parhand checked, as we did for the disjunctive
allel corpora. In Eighth International Conferenceentries. In this case the verbal form can beon Theoretical and Methodological Issues in Machine
deduced from the nominal one using Nomlex
Translation: TMI-99, pages 88–97. Chester, UK.
(Macleod et al., 1998) or WordNet 2.0. However, there are over 10,000 verbal nouns, so Kyonghee Paik, Francis Bond, and Satoshi Shirai.
2001. Using multiple pivots to align Korean andsemi-automatic checking becomes even more
Japanese lexical resources. In Workshop on Lanimportant.

>ç×

rG *l.

G *l.

rguage Resources in Asia, NLPRS-2001, pages 63–70.
Tokyo.

7Conclusion

Jonas Sjöbergh. 2005.
Creating a free digital Japanese-Swedish lexicon.
In ProceedIn this paper we showed a semi-automatic apings of PACLING 2005, pages 296–300. Tokyo,
proach (automatic generation followed by manualJapan.
URL \url{http://www.nada.kth.se/checking) to improve disjunctive entries in the JM~jsh/publications/jlex.pdf}.

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Masao Utiyama and Mayumi Takahashi. 2003.
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