Japanese SemCor: A Sense-tagged Corpus of JapaneseFrancis Bond,∗,∗∗∗ Timothy Baldwin,∗∗ Richard Fothergill∗∗ and Kiyotaka Uchimoto∗∗∗∗
Linguistics and Multilingual Studies, Nanyang Technological University, Singapore∗∗
Computer Science and Software Engineering, Melbourne University, Australia∗∗∗
National Institute of Information and Communications Technology, Japanbond@ieee.org,tb@ldwin.net,richard.fothergill@gmail.com,uchimoto@nict.go.jp

Abstract
In this paper we describe the creation of
the Japanese SemCor (JS EM C OR) sensetagged corpus of Japanese. The corpus
is a translation of the English S EM C OR,
with senses projected across from English. The final corpus consists of 14,169
sentences with 150,555 content words ofwhich 58,265 are sense tagged. The corpus is one of the corpora used to provide sense frequency data for the JapaneseWordnet.

1 Introduction
Wordnets have been shown to have utility acrossa broad range of applications, largely in combination with sense frequency data and sense-tagged
corpora. This paper describes Japanese SemCor (JS EM C OR), a sense-tagged corpus for theJapanese Wordnet (Isahara et al., 2008), based ontranslation of the English S EM C OR and sense projection.
In order to produce annotated text quickly andcheaply, we adopt the method of annotationtransfer pioneered in the Italian M ULTI S EM C OR
(Bentivogli and Pianta, 2005). In this approach,a sense-tagged text in one language is translated
into the language in question, and the sense annotations from the original corpus are projected
onto the new language. The sense projection isbased on a wordnet in the target language whichis aligned with the wordnet that was used to sense
tag the source language text. Bentivogli and Pianta(2005) found that annotation transfer led to sense
tagging with a precision of 86% and coverage of
81% (that is 19% of open class words still needed
to be annotated), at less cost than annotating fromscratch. The main differences in our case are: (1)the target language (Japanese) is linguistically further removed from the source language (English);and (2) to boost coverage, we provide the translators with sense-specific translations of each openclass words to optionally include in their translations.
Similarly to M ULTI S EM C OR, our method takes
English S EM C OR and translates it into the target language. In addition to the immediate objective of deriving a sense-tagged corpus of Japanese
based on Japanese Wordnet, we also create a trilingual (English–Italian–Japanese) sensebank, withpotential applications in other tasks such as translation. Because the Japanese (and Italian) texts aretranslated, the sense distribution may not be truly
representative of native Japanese text. Ultimately,
we aim to supplement JS EM C OR with other sensetagged data, based on native Japanese text.
Finally, in the same way that the English and
Italian annotation revealed missing word senses
for their respective wordnets, we expect to find andcorrect such errors in the Japanese Wordnet, which
will be fed back to the developers.
In the next section we give a brief description of
the base resources used in the creation of JS EM C OR. Next, we describe the creation, size and distribution of JS EM C OR (§3). Finally, we discuss
future work (§4) and then conclude.

2 Resources
2.1 The English Wordnet
The wordnet used both to tag the English S EM C OR corpus (§2.3) and as the backbone of theJapanese wordnet (§2.2) is the Princeton WordNetof English (Fellbaum, 1998). S EM C OR is taggedwith tags from version 1.6 and the Japanese wordnet aligns with version 3.0. PWN has a rich structure of semantic relations, but we are only using it
as a source of sense inventories in this task.

2.2

The Japanese Wordnet

The Japanese Wordnet is a large scale, freely available, semantic dictionary of Japanese. The National Institute of Information and Communications Technology (NICT) started developing theJapanese Wordnet in 2006, as part of its support for Natural Language Processing researchin Japan. The first version (0.9) was releasedin February 2009. In the initial phase Japaneseequivalents were added to synsets of the Princeton WordNet. These have been expanded andcorrected in subsequent releases. The current release is version 1.1. It contains 57,238 synsets(concepts), 93,834 unique Japanese words and
158,058 senses (synset–word pairs). All synsets
have Japanese definitions, and over 45,000 also
have examples.
We give an example of an entry in Figure 1.
From the beginning, the Japanese Wordnet
project planned to tag text in order to verify its
coverage and get distribution information (Isahara
et al., 2008), but no tagged text has been releasedso far.
2.3

SemCor and MultiSemCor

The English S EM C OR corpus is a sense-tagged
corpus of English created at Princeton University
by the WordNet Project research team (Landes
et al., 1998). It was created very early in the
WordNet project, and was one of the first sensetagged corpora produced for any language. The
corpus consists of a subset of the Brown Corpus(Francis and Kucera, 1979), and has been part-ofspeech tagged and sense tagged. We use the subset of S EM C OR which was translated into Italianas part of M ULTI S EM C OR 1.1 (Bentivogli and Pianta, 2005).
Synset

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

M ULTI S EM C OR is an English/Italian parallel
corpus created by translating the English S EM C OR corpus into Italian. Texts are aligned at thesentence and word level, and annotated with partof speech, lemma and word sense (PWN 1.6).
M ULTI S EM C OR version 1.1 contains 116 English
texts: 14,144 sentences and 261,283 tokens, ofwhich 119,802 tokens are annotated with senses.
These are aligned with their corresponding Italiantranslations. In this paper we only use the English
texts, which are freely available.1 The M ULTI S EM C OR team reports tag errors for around 2.5%of the English open-class tokens in the English
S EM C OR (Bentivogli and Pianta, 2005).

3 Japanese SemCor
The initial data for the translation was created bytaking the English S EM C OR data and mapping thesenses to version 3.0 using the mappings createdby Daude et al. (2003). These senses were thenused to look up synsets in the Japanese Wordnet
to be presented to the translators.
3.1 Creation
Similar to M ULTI S EM C OR, sense annotation in
JS EM C OR was set up as a translation task, wheretranslators were provided with a S EM C OR sentence in English and asked to generate a Japanesetranslation using the interface depicted in Figure 2.
For each sense-indexed word in the original S EM C OR data, we provided translators with a list of
all words contained in the corresponding JapaneseWordnet synset. Clicking on one of these words
both appended that lemma to the translation, andrecorded clickthrough data for the word, which
1
The Italian texts are available free for researchfrom the Istituto Trentino Di Cultura (ITC) http://
multisemcor.itc.it.

02076196-n












ja 海豹, アザラシ, シールanimal/seal.png
en seal“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

Figure 1: Example Entry for Seal/海豹

Figure 2: Screen shot of the annotation interface, for the S EM C OR sentence Remove the child from thescene of his misbehaviour
provided the basis for the ultimate sense tags in
the translation. Translators also had the option toleave a comment (e.g. if they wanted to note something about the translation lists, or the source English), to mark their lack of confidence in a translation (via the “Unsure” checkbox), or to leave atranslation to come back to (via the “Hold” checkbox).
In the example in Figure 2, shown in (1),the translator has used the words 子 供 kodomo“child” and 現場 geNba “scene”, but has not madeuse of any of the translations for remove or misbehavior in their final translation (as indicated bothby the lack of a highlighted translation and theticked checkboxes for the respective words). They
had the option of adding the new translation of remove to the synset, but did not use it here. The single word misbehavior was translated as the multiword expression 悪い こと を した warui-koto-

wo shita “did something bad”, probably because
all of the translations of misbehavior sound morecriminal than their English equivalents, making
them inappropriate for child behaviour.
(1)a. Removea1 the childb1 from the scenec1 of
his misbehavior1 .
b. 悪い? こと? を した? 子供b1
warui koto wo shita kodomo
bad thing ACC done childを 現場c1 から 離すa1 。
wo genba kara hanasu .
ACC scene from remove .

The final result for this sentence is that, of thefour sense-tagged words in the original, two words
have their sense transferred (b,c ), one word could
be transferred if the new lemma were added to it(a ), and one word gets translated into three, noneof which can be easily linked.

At the outset of the translation process, sentences were allocated to translators from a global
sentence queue, meaning that if two translators
were working in tandem, a given translator would
often not translate contiguous sentences from agiven document, potentially leading to lack of coherence in the translations. While translation coherence was not of primary interest, we switched
across to allocating data from a document queue
about 20% of the way through the translation process, in response to concerns over the resultant
consistency in sense annotations within a given
document, and requests from the translators. As
part of this, we provided support for a “Documentview”, to allow the translator to look over a document in its entirety, including whatever progress
had been made through the translation. We also
gave translators the option of viewing the source
English and translation for the immediately preceding sentence (Remove temptations and 疑 惑を払うこと giwaku-o harau koto, resp., in Figure 2). They could also view their past 10 translations via the pulldown menu at the top-left of thetranslation page.
Translators were instructed to use translations
provided in the list where possible, in order tomaximise sense tagging coverage, except where
this led to stilted Japanese (e.g. in translating an
English deictic pronoun literally, rather than using a zero pronoun). The purpose of translation lists and the need for the clickthrough data
was explained to the translators, although noneof the translators were computational linguists, so
the significance of sense tagging and the resulting
sense-tagged corpus wasn’t self-evident to them.
Translators were also instructed to:• use formal “editorial” Japanese, e.g. using
the である dearu form of the copular, unlessthe text was clearly written in a colloquial orother style;• attempt to determine the canonical translation/transliteration of proper names where
possible, and failing this, to transliterate,
flagging the translation as “Hold” if unsure of
the pronunciation; acronyms were to be left
as is, unless there was a well-known Japanese
rendering of the acronym (e.g. METI for 通産 省 tsūsaNshō);• refrain from including alternative translations, e.g. in parentheses, in cases of doubt;

CorpusSentences
Words
Content Words

S EM C OR
12,842
261,283
119,802

JS EM C OR
14,169382,762
150,555

Table 1: Corpus Size
• be faithful to the English sentence tokenisation (i.e. never translate multiple English sentences into a single Japanese translation), but
to translate into multiple sentences in cases
where it improved readability (e.g. for particularly long or heavily embedded English sentences);• reorder the words where necessary to maximise readability in Japanese (esp. for conjunctions of nouns or adjectives);• include discourse connectives where it improved overall sentence and document readability, irrespective of whether a corresponding sentential adverb (or equivalent) was included in the original English sentence.
3.2 Statistics
In contrast to Bentivogli and Pianta (2005), we
have used manual rather than automatic wordalignment. However, the alignment requires somepost-processing before annotation transfer can occur. In this section, we look at various statistics ofthe alignment and annotation transfer process.
The word-alignment clickthrough data produced by our translators maps tokens in S EM C OR
to lemmas in Japanese Wordnet, within the context
of a translated sentence. In the following, we referto a translated lemma in context as a translation
lemma. Each translation lemma must be mapped
onto the text of the translated sentence to complete
the word alignment.
We perform this mapping automatically by firsttokenising the sentence with the morphologicalanalyser MeCab using the IPAdic lexicon andtagset (Kudo et al., 2004) and using the part ofspeech and lemma information it provides. This
results in 382,762 tokens overall and 148,249 openclass tokens, giving averages of 27 and 10.5 per
sentence respectively.
The segmentation MeCab produces is finegrained relative to both English and to theJapanese Wordnet — in particular splitting compounds into their components — so we map trans-lation lemmas to sequences of tokens. We accept a sequence of tokens as a match for a Princeton WordNet lemma if all parts in the translation
match in their canonical word order, optionally allowing the final token to be in its lemmatised form,
which is a convenient heuristic for lemmatising
Japanese compounds. The numbers are summarized in Table 1.
Of 61,827 translation lemmas available, 7,551
are compounds with respect to IPAdic. Of the rest,
44,813 are single token and 9,463 are not foundin the translation: the translation interface allowed
free editing of the translation text but did not allow
clickthrough word alignments to be undone.
Note that the resulting word alignment is not
one-to-one: 1,734 translation lemmas come from
more than one source word, though only 190 comefrom more than one source lemma (and none from
more than two). Conversely, 3,252 translation
lemmas match more than once in the translated
sentence. Also, the alignment coverage is notcomplete: 51,450 sense tagged tokens in S EM C OR
have not been translated, and 90,525 open classtokens in the Japanese sentence translations haveno translation lemma mapped to them. Part ofspeech distributions for unaligned tokens in bothlanguages are shown in Table 2.
After completion of the word alignment, we
perform the annotation transfer. For a number ofreasons, annotation transfer can result in zero or
multiple senses being assigned to a word-aligned
translation:• Due to rearrangement of senses between
WordNet versions 1.6 and 3.0, some S EM C OR tokens are annoted with deleted senses
and others with more than one sense.
• We introduce additional variation in annotation multiplicity with a many-to-many wordalignment.
• The presence in the translation data of usercontributed word translations means that analigned word is not always in the transferredsynset in Japanse Wordnet. In fact, this occurs 13,857 times, suggesting a large number of potential new synset memberships forJapanese Wordnet.
Therefore, of the 61,827 translation lemmas, 131
are assigned more than one sense and 13,771 havenone. The remaining 47,925 translation lemmas

are assigned a single sense. After taking into account translation lemmas which appear more thanonce — or not at all — in the target sentence,
46,121 words receive tags from the annotationtransfer.
Due to the granularity mismatch between
IPAdic and Japanese Wordnet, we take the additional step of mapping Japanese Wordnet lemmas to portions of text without word-aligned translations. The resulting compounds (or single tokens) do not receive a sense tag but are annotated with Japanese Wordnet lemma and part ofspeech. Where potential matches overlap, precedence is given first to longer matches (e.g., 米
国 政 府 beiko-kuseifu “Washington” is chosenover 政府 seifu “government”) and then to earlier
matches (e.g. 近代化 kiNdaika “modernisation”
is chosen over 化する ka-suru “to change” where
they intersect in the out-of-vocabulary 近代化す
る kiNdaika-suru “to modernise”). This process
produces an additional 61,495 unaligned words.
We then include open class MeCab tokens whichhave still not been assigned a Japanese Wordnet
lemma as an additional 34,329 words.
Finally, there are 12,144 monosemous Japanesewords (with only a single sense) which were notannotated in the translation process, either because
sense transfer fails or because the word is notaligned. Applying these single sense annotations
brings the total number of sense annotated words
to 58,265.

3.3 Distribution
We use the Kyoto Annotation Format (KAF) to
share the corpus (Bosma et al., 2009). This isan emerging standard for wordnet annotation. We
only use the two lowest layers (text and term),
not including any higher levels such as dependencies or geodata. In order to make the data
accessible, we will release it under the same license as the English S EM C OR. JS EM C OR is distributed with the Japanese Wordnet, available fromhttp://nlpwww.nict.go.jp/wn-ja/.
A sample KAF record is presented in Figure 3,
containing two words with Japanese Wordnet
senses (学 校 gakkō “school” and 戻 る modoru“return”), IPAdic part-of-speech tags for all tokens, and file and sentence IDs which align withEnglish S EM C OR.

Part of Speech
Verb
Noun
Adjective
AdverbEnglish Tokens
13,457
9,979
10,337
12,321

Japanese Tokens
24,69841,394
2,794
5,635

Table 2: Part of speech distribution for tokens without word alignment

4 Discussion and Future WorkWe were able to transfer far fewer senses than the
M ULTI S EM C OR (39% vs. 81%). One major reason for this is that the missing terms that this annotation project has found have not yet been added to
the Japanese Wordnet. Adding them will raise thecoverage by another 9%. Another reason is that weare currently overcounting untagged senses — if aword should be tagged as a multiword expression
we count is once as the MWE and once for eachof the single terms. However, the greatest reason
is the fundamental differences between Japaneseand English. There were three major causes thatmade transfer impossible. The first is that in manycases a word-for-word translation is unnatural —either there is a lexical gap in Japanese so that the
English term does not have any translation, or the
direct translation has a different connotation.
A major cause of lexical gaps is part-of-speechmismatches. For example, the English Wordnet
has these three entries for French:2 French n 1“a native or inhabitant of France”; French n 2“the Romance language spoken in France” andFrench a 1 “of or pertaining to France or thepeople of France”. In Japanese, the first two
are productive multiword expressions furansujiN “France person” and furansu-go “France language” and the third is made by adding the postposition no “of” to either of these or just to France:
furansu-jiN-no “French (person) lit: France person of”, furansu-go-no “French (language) lit:
France language of” and furansu-no “French(other) lit: France of”. Because these postpositional phrases are completely compositional,
it seems redundant to list them in the JapaneseWordnet. In addition, to align accurately, we
would have to either separate the current adjectivesynset into three senses: “of or pertaining to thelanguage of France”; “of or pertaining to the people of France” and “of or pertaining to the France”
possibly with the third as the hypernym of the first2
In addition there are two more which are not relevant tothis discussion.

two. A better approach may be to take advantageof the rich structure of the current wordnet andallow alignment between furansu “France” andFrench through the pertainym relation (French a 1
pertains-to France n 1). However, currently there
is no easy way to link furansu-go “French n 1(Language)” with French a 1. Perhaps the proper
solution is to add additional pertainym links:
French a 1 pertains-to French n 1 (language) and
French a 1 pertains-to French n 2 (people). Notethat similar differences exist, of course, between
English and Italian, but they occur far less often due to greater similarity between the two languages.
We have a rich source of new senses suggestedby the translators (13,857 cases) that can be used
to extend the cover of the Japanese Wordnet. For
example, in Figure 2, remove is translated as 離
す hanasu, even though this word was not oneof the synonyms for that synset in the JapaneseWordnet. A preliminary investigation of thesefound that, in all cases, something had to be added
to the wordnet, and in 60% of the cases the suggested translation could be used as is. The remaining cases fall into three groups (similar to those
discussed above): loose translations which do notreally refer to the same synset; Japanese tokens
which should be part of a larger multiword expression; and translations which change the partof speech. In addition, we found some errors in
the English sense tagging.
In future work, we intend to investigate techniques for efficiently correcting any remaining errors in the corpus. As much as possible, we would
like to fix errors in both English and Japanese,so that we can start to carry out quantitative contrastive semantic analysis.
We would also like to investigate how ambiguities are distributed across different languages. For
example, 歯 ha “tooth” is used for human teeth
and cogwheel teeth in English, Japanese and Italian: all three languages share the same ambiguity.
In general, we expect to find less ambiguity shared<?xml version="1.0" encoding="utf8"?><KAF lang="jpn"><kafHeader><fileDesc filename="br-k01"/><linguisticProcessors layer="text"><lp timestamp="2011-09-23T11:45:18" version="0.98" name="MeCab"/></linguisticProcessors></kafHeader><text><wf wid="w1.1.1" sent="1" para="1">スコッティ</wf><wf wid="w1.1.2" sent="1" para="1">は</wf><wf wid="w1.1.3" sent="1" para="1">学校</wf><wf wid="w1.1.4" sent="1" para="1">に</wf><wf wid="w1.1.5" sent="1" para="1">戻ら</wf><wf wid="w1.1.6" sent="1" para="1">なかっ</wf><wf wid="w1.1.7" sent="1" para="1">た</wf><wf wid="w1.1.8" sent="1" para="1">。</wf></text><terms><term tid="t1.1.1" lemma="スコッティ" type="open" pos="N.名詞.一般"><span><target id="w1.1.1"/></span><component lemma="スコッティ" id="c1.1.1" pos="N.名詞.一般"/></term><term tid="t1.1.3" lemma="学校" type="open" pos="N.n"><span><target id="w1.1.3"/></span><component lemma="学校" id="c1.1.3" pos="N.名詞.一般"/><externalReferences><externalRef resource="Wordnet jpn 1.1" reference="jpn-11-学校-n"/></externalReferences></term><term tid="t1.1.5" lemma="戻る" type="open" pos="V.v"><span><target id="w1.1.5"/></span><component lemma="戻る" id="c1.1.5" pos="V.動詞.自立"/><externalReferences><externalRef resource="Wordnet jpn 1.1" reference="jpn-11-戻る-v"/></externalReferences></term></terms></KAF>

Figure 3: Sample KAF record for スコ ッ ティ は 学校 に 戻ら なかっ た 。 Scotty ha gakkō ni
modora nakat ta ., the Japanese translation of English sentence Scotty did not go back to school
between languages from very different families(such as Japanese and English/Italian), but there
is also extensive borrowing between English andJapanese. With a sense-tagged tritext, we can start
to investigate these questions.

References

5 Conclusion

Wauter Bosma, Piek Vossen, Aitor Soroa, German
Rigau, Maurizio Tesconi, Andrea Marchetti, Monica Monachini, and Carlo Aliprandi. 2009. KAF: ageneric semantic annotation format. In Proceedingsof the 5th International Conference on Generative
Approaches to the Lexicon (GL 2009), Pisa.

In this paper we described the creation of theJapanese Semantic Corpus JS EM C OR. The corpus is a translation of the English S EM C OR, withsenses projected across from English. The final
corpus consists of 14,169 sentences with 150,555
content words of which 58,265 are sense tagged.

Luisa Bentivogli and Emanuele Pianta. 2005. Exploiting parallel texts in the creation of multilingual
semantically annotated resources: the MultiSemCor
Corpus. Natural Language Engineering, 11(3):247–
261, sep. Special Issue on Parallel Texts.

Jordi Daude, Lluis Padro, and German Rigau. 2003.
Validation and tuning of Wordnet mapping techniques. In Proceedings of the International Confer-ence on Recent Advances in Natural Language Processing (RANLP’03), Borovets, Bulgaria.
Christine Fellbaum, editor. 1998. WordNet: An Electronic Lexical Database. MIT Press.
W. Nelson Francis and Henry Kucera, 1979. BROWNCORPUS MANUAL. Brown University, Rhode Island, 3 edition. (http://khnt.aksis.uib.
no/icame/manuals/brown/).
Hitoshi Isahara, Francis Bond, Kiyotaka Uchimoto,
Masao Utiyama, and Kyoko Kanzaki. 2008. Development of the Japanese WordNet. In Sixth International conference on Language Resources and
Evaluation (LREC 2008), Marrakech.
Taku Kudo, Kaoru Yamamoto, and Yuji Matsumoto.
2004. Applying Conditional Random Fields toJapanese Morphological Analysis. In Dekang Lin
and Dekai Wu, editors, Proceedings of EMNLP
2004, pages 230–237, Barcelona, Spain, July. Association for Computational Linguistics.
Shari Landes, Claudia Leacock, and Christiane Fellbaum. 1998. Building semantic concordances. In
Fellbaum (Fellbaum, 1998), chapter 8, pages 199–
216.