Extending the Japanese WordNet
Francis Bond,† Hitoshi Isahara,‡ Kiyotaka Uchimoto,††Takayuki Kuribayashi† and Kyoko Kanzaki‡†NICT Language Infrastructure Group, ‡ NICT Language Translation GroupMASTAR Project
National Institute of Information and Communications Technology<{bond,isahara,uchimoto,kuribayashi,kanzaki}@nict.go.jp>
1

Introduction

Our goal is to make a semantic lexicon of Japanesethat is both accesible and usable. To this end
we are constructing and releasing the Japanese
WordNet (WN-Ja) (Bond et al., 2008b,a).
We have almost completed the first stage,
where we automatically translated the English
and Euro WordNets, and are hand correcting it.
We introduce this in Section 2. Currently, we areextending it in three main areas: the first is to add
more concepts to the Japanese WordNet, either
by adding Japanese to existing English synsets orby creating new synsets (§ 3). The second is tolink the synsets to text examples (§ 4). Finally,
we are linking it to other resources: the semantic lexicon GoiTaikei (Ikehara et al., 1997) anda collection of illustrations taken from the OpenClipArt Library (Phillips, 2005) (§ 5).

2

Current State

Currently, the WN-Ja consists of 157,646 senses(word-synset pairs) 36,922 concepts (synsets) and
73,113 unique Japanese words. The relationalstructure (hypernym, meronym, domain, . . . ) isbased entirely on the English WordNet 3.0 (Fellbaum, 1998). Of these entries, 81% have beenchecked by hand, 11% were automatically created by linking through multiple languages and
8% were automatically created by adding nonambiguous translations, as described in Bondet al. (2008a). For up-to-date information on
WN-Ja see: nlpwww.nict.go.jp/wn-ja.
An example of the entry for the synset
02076196-n is shown in Figure 1. Most fields
come from the English WordNet. We have added
the underlined fields (Ja Synonyms, Illustration,GoiTaikei) and are currently adding the translated gloss. In the initial automatic construction there were 27 Japanese words associated withthe synset,1 including many inappropriate translations for other senses of seal (e.g., 判こ hanko“stamp”). These were reduced to three after
checking: ア ザ ラ シ, 海 豹 azarashi “seal” andシール shi-ru “seal”.
The main focus of this year’s work has been thistrimming of badly translated words. The result isa WordNet with a reasonable coverage of common
Japanese words. The precision per sense to bejust over 90%. We have aimed at high coverageat the cost of precision for two reasons: (i) we
think that the WordNet must have a reasonable
coverage to be useful for NLP tasks and (ii) we
expect to continue refining the accuracy over thefollowing years.

3

Increasing Coverage

We are increasing the coverage in two ways. The
first is to continue to manually correct the automatically translated synsets: there are still some
27,000 unchecked synsets. More interestingly, wewish to add synsets for Japanese concepts that
may not be expressed in the English WordNet.
To decide which new concepts to add, we will beguided by the other tasks we are are doing: annotation and linking. We intend to create new
synsets for words found in the corpora we annotate that are not currently covered, as well forconcepts that we want to link to. An example forthe first is the concept 御飯 gohan “cooked rice”,
as opposed to the grain 米 kome “rice”. An example of the second is シングル shinguru “single:
a song usually extracted from a current or upcoming album to promote the album”. This is avery common hypernym in Wikipedia but missing
from the English WordNet.
アザラシ, シール, スタンプ, 上封, 判, 判こ, 判子, 刻
印, 加判, 印, 印判, 印形, 印章, 印鎰, 印鑑, 印鑰, 印顆, 墨引, 墨引き, 封, 封じ目, 封印, 封目, 封着, 封緘, 押し 手, 押印, 押手, 押捺, 捺印, 極印, 海豹, 版行, 符節, 緘, 証印, 調印1

SynsetSynonymsDef (en)Def (ja)
HypernymsHyponyms
GoiTaikei

02076196-n






animal/seal.png
“any of numerous marine mammals that come on shore to breed; chieﬂy of cold regions”
「繁殖のために岸に上がる海洋性哺乳動物の各種；主に寒帯地域に」
アシカ亜目/pinniped？/crabeater seal ？/eared seal 海驢/earless seal

ja
en
fr

海豹, アザラシ, シールsealphoquehh537:beastii

Figure 1: Example Entry for Seal/海豹
Name
SemcorGlosses
Kyoto

Sentences
12,842
165,977
38,383

Words
224,260
1,468,347
969,558

Content Words
120,000
459,000
527,000

Table 1: Corpora to be Sense Tagged
As far as possible, we want to coordinate thecreation of new synsets with other projects: forexample KorLex: the Korean WordNet alreadymakes the cooked rice/grain distinction, and thePrinceton WordNet should also have a synset forthis sense of single.

4Text Annotation

We are in the process of annotating three texts(Table 1). The first two are translations of WordNet annotated English Texts (SemCor and theWordNet glosses), the third is the Japanese newspaper text that forms the Kyoto Corpus. We
expect to finish translating and annotate all ofSemCor, translate the WordNet glosses and startannotation on the Kyoto Corpus in 2009.
This annotation is essential for finding missing
senses in the Japanese WordNet, as well as getting the sense distributions that are needed forsupervised word sense disambiguation.
4.1

SemCorSemcor is a textual corpus in which words havebeen both syntactically and semantically tagged.
The texts included in Semcor were extracted fromthe Brown corpus (Francis and Kucera, 1979)
and then linked to senses in the Princeton WordNet. The frequencies in this corpus were used togive the sense frequencies in WordNet (Fellbaum,1998). A subset of this corpus (MultiSemCor)
was translated into Italian and used as a corpus

for the Italian WordNet (Bentivogli et al., 2004).
We are translating this subset into Japanese.
In the same way as Bentivogli et al. (2004), weare exploiting Cross-Language Annotation Transfer to seed the Japanese annotation. For example, consider (1)2 . The content words answer,
was, simple, honest are tagged in Semcor. They
can be aligned with their translations 答え kotae“answer” 簡単 kantan “simple”, 率直 socchoku“honest” and だった datta “was”. This allows
us to tag the Japanese translation with the same
synsets as the English(1) His answeri wasj simplek but honestl .
答えi は 簡単k ながらも 率直l な ものだったj 。
We chose a translated Semcor as the basis of annotation for two main reasons: (i) the corpus canbe freely redistributed — we expect the glosses
to be useful as an aligned corpus — and (ii) it
has several other annotations associated with it:
Brown corpus POS annotation, Penn Treebank
syntactic annotation, and the Italian Translations
from the MultiSemCor corpus.
4.2

WordNet glosses

Our second translated corpus is formed from theWordNet glosses (and example sentences) themselves (e.g., the def field shown in Figure 1). The
English glosses have also been annotated withword senses as the Princeton WordNet Gloss Corpus. In the same way that we do for SemCor, weare translating the glosses and seeding the annotations.
Using the glosses as the base for a sense annotated corpus is attractive for the following rea2

Sentence 96 in b13.

sons: (i) the translated corpus can be freely redistributed — we expect the glosses to be useful asan aligned corpus and also to be useful for manyother open lexicons; (ii) the glosses are useful for
Japanese native speakers using the WordNet, (iii)the glosses are useful for unsupervised sense disambiguation techniques such as LESK (Baldwinet al., 2008) and (iv) other projects have alsotranslated synset glosses (e.g. Spanish and Korean), so we can hope to create a multilingualcorpus here as well.
4.3

Kyoto Text Corpus

The Kyoto Text corpus consists of newspaper text
from the Mainichi Newspaper 1995, segmentedand annotated with Japanese POS tags and dependency trees (Kurohashi and Nagao, 2003). We
hope to annotate at least parts of it during 2009.
Even though the Kyoto Text Corpus is notfreely redistributable, we have chosen to annotate it due to the wealth of annotation associatedwith it: dependency trees, predicate-argument relations and co-reference (Iida et al., 2007), translations into English and Chinese (Uchimoto et al.,2004) and sense annotations from the Hinoki
project (Bond et al., 2006). We also felt it wasimportant to tag some native Japanese text, notonly translated text.

5Linking to other resources

In our initial release, we link WordNet to two
other resources: Nihongo GoiTaikei (Ikeharaet al., 1997) and a collection of pictures from theOpen Clip Art Library (OCAL: Phillips (2005)).
The basic approach is to find confident matches
automatically and then generalize from them. We
find matches in three ways:
MM Monosemous monolingual matchese.g. cricket bat or 海豹
MB Monosemous bilingual matchese.g. h海豹↔seal i
HH Hypernym/Hyponym pairs
e.g. hseal ⊂ mammali
Similarly, we will also link the concepts fromthe EDR lexicon (EDR, 1990) and the hypernymhyponym links from Torishiki-kai (Kuroda et al.,2009).

5.1

GoiTaikei

Linking Goi-Taikei, we used not only the Japanesedictionary published in Ikehara et al. (1997), butalso the Japanese-English dictionary used in themachine translation system ALT-J/E (Ikeharaet al., 1991). We attempted to match synsets
to semantic categories by matching the Japanese,
English and English-Japanese pairs to unambiguous entries in Goi-Taikei. For example, the synset
shown in Figure 1 was automatically assigned thesemantic category hh537:beastii, as 海豹 appears
only once in WN-Ja, with the synset shown, andonce in the Japanese dictionary for ALT-J/E witha single semantic category.
We are currently evaluating our results against
an earlier attempt to link WordNet and GoiTaikei
that also matched synset entries to words in GoiTaikei (Asanoma, 2001), but did not add an extra
constraint (that they must be either monosemousor match as a hypernym-hyponym pair).
5.2

Open ClipArt Library

In order to make the sense distinctions morevisible we also semi-automatically link synsets
to illustrations from the Open Clip Art Library(OCAL: Phillips (2005)). This adds a new modality to the knowledge linked in the semantic net.
Illustrations of concepts are useful for a varietyof tasks. One is pedagogical — it is useful tohave pictures in learners’ dictionaries. Anotheris in cross-cultural communication - for example in Pangea, where children use pictons (smallconcept representing pictures) to write messages(Takasaki and Mori, 2007).
We use the OCAL collection distributed as
SVG (scalable vector graphic) images in theUbuntu Fiesty distribution based on the releaseof October 2005 (v 0.18). It contains 6,826 uniqueimages, organized in a shallow file hierarchy.
Each image is associated with a collection ofexplicit metadata, including a title, description
and a set of tags, all of which are recommended
rather than obligatory. We consider the title andsimplified basename to be the entry for an illustration, and its tags the hypernyms. For example, for seal.svg, its title is Etiquette Icons andit is tagged as animal and mammal . We lookin wordnet for hypernym synsets of seal that include mammal and find the following: seal#n#9

⊂ placental#n#1 ⊂ mammal#n#1. Therefore, thispicture illustrates seal#n#9 rather than the othersynsets associated with the word seal
There are several sources of metadata. We firstuse explicit metadata such as title for the root
word and tags for the hypernyms. If there isno explicit metadata (which is true for around athird of the images) or we couldn’t find a match,then we look for implicit metadata. We take thebasename seal and delete any numbers from theend. We also add directory names to the tag list
(animals/seal.svg): in this case “animal”.
We have only linked a small subset of illustrations (936 out of 6,826 images) and an evensmaller proportion of wordnet (758 out of 82,115
noun synsets). However, because any illustrated
synset also (in theory) illustrates its hypernyms,
we have indirectly illustrated far more than 758
synsets: these figures are better than they seem.

6Discussion

We we presented the current state of the JapaneseWordNet (157,646 senses, 36,922 concepts and73,113 unique Japanese words, with links to GoiTaikei and OCAL) and outlined our plans for further work (more words, links to corpora and otherresources). We hope that WN-Ja will become auseful resource not only for natural language processing, but also for teaching and linguistic research.
Acknowledgments We would like to thank NTT, inparticular Sanae Fujita, for their cooperation in linking tothe Goi-Taikei.

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