Automatic WordNet mapping using word sense disambiguation* Changki Lee Seo JungYun Geunbae Leer Natural Language Processing Lab Natural Language Processing Lab Dept. of Computer Science and Engineering Dept. of Computer Science Pohang University of Science & Technology Sogang University San 31, Hyoja-Dong, Pohang, 790-784, Korea Sinsu-dong 1, Mapo-gu, Seoul, Korea {leeck,gblee }@postech.ac.kr seojy@ ccs.sogang.ac.kr bilingual Korean-English dictionary. The first Abstract sense of 'gwan-mog' has 'bush' as a translation in English and 'bush' has five synsets in This paper presents the automatic WordNet. Therefore the first sense of construction of a Korean WordNet from 'gwan-mog" has five candidate synsets. pre-existing lexical resources. A set of Somehow we decide a synset {shrub, bush} automatic WSD techniques is described for among five candidate synsets and link the sense linking Korean words collected from a of 'gwan-mog' to this synset. bilingual MRD to English WordNet synsets. As seen from this example, when we link the We will show how individual linking senses of Korean words to WordNet synsets, provided by each WSD method is then there are semantic ambiguities. To remove the combined to produce a Korean WordNet for ambiguities we develop new word sense nouns. disambiguation heuristics and automatic mapping method to construct Korean WordNet based on 1 Introduction the existing English WordNet. There is no doubt on the increasing This paper is organized as follows. In section 2, importance of using wide coverage ontologies we describe multiple heuristics for word sense for NLP tasks especially for information disambiguation for sense linking. In section 3, we retrieval and cross-language information explain the method of combination for these retrieval. While these ontologies exist in English, heuristics. Section 4 presents some experiment there are very few available wide range results, and section 5 will discuss some related ontologies for other languages. Manual researches. Finally we draw some conclusions construction of the ontology by experts is the and future researches in section 6. The automatic most reliable technique but is costly and highly mapping-based Korean WordNet plays a natural time-consuming. This is the reason for many Korean-English bilingual thesaurus, so it will be researchers having focused on massive directly applied to Korean-English cross-lingual acquisition of lexical knowledge and semantic information retrieval as well as Korean information from pre-existing lexical resources monolingual information retrieval. as automatically as possible. This paper presents a novel approach for 2 Multiple heuristics for word sense automatic WordNet mapping using word sense disambiguation disambiguafion. The method has been applied to As the mapping method described in this paper link Korean words from a bilingual dictionary to has been developed for combining multiple English WordNet synsets. individual solutions, each single heuristic must be To clarify the description, an example is given. seen as a container for some part of the linguistic To link the first sense of Korean word knowledge needed to disarnbiguate the "gwan-mog" to WordNet synset, we employ a * This research was supported by KOSEF special purpose basic research (1997.9- 2000.8 #970-1020-301-3) Corresponding author 142 ambiguous WordNet synsets. Therefore, not a candidate synsets in smaller number of single heuristic is suitable to all Korean words translations get relatively less weight (a=0.5 was collected from a bilingual dictionary. We will tuned experimentally), support(s,ew) calculates describe each individual WSD (word sense the maximum similarity with the synset s and the disambiguation) heuristic for Korean word translation ew, which is defined as : mapping into corresponding English senses. support(si, ew) = max S(si, s) Korean word English word WordNetsynset sEsynset( ew ) wsl S2) = ~ S im( st, s2) if sire(s,, s2) _> 0 S(sl, = WS 2 l 0 otherwise ... Similarity measures lower than a threshold 0 are considered to be noise and are ignored. In our experiments, 0=0.3 was used. sim(s,s2) computes W$ n the conceptual similarity between concepts s~ and sz as in the following formula : Figure 1: Word-to-Concept Association 2 x level(MSCA(sl, s:)) sim(sl, s2)= Figure 1 shows the Korean word to WordNet level(sO + level(s2) synset association. The j-th sense of Korean word kw has m translations in English and n where MSCA(sl,s2) represents the most specific WordNet synsets as candidate senses. Each common ancestor of concepts s~ and s2 and heuristic is applied to the candidate senses (ws,, level(s) refers to the depth of concept s from the .... ws) and provides scores for them. root node in the WordNetL 2.1 Heuristic 1: Maximum Similarity 2.2 Heuristic 2: Prior Probability This heuristic comes from our previous This heuristic provides prior probability to Korean WSD research (Lee and Lee, 2000) and each sense of a single translation as score. assumes that all the translations in English for Therefore we will give maximum score to the the same Korean word sense are semantically synset of a monosemous translation, that is, the similar. So this heuristic provides the maximum translation which has only one corresponding score to the sense that is most similar to the synset. The following formula explains the idea. senses of the other translations. This heuristic is applied when the number of translations for the H2(s,) = max P(s, l ew) same Korean word sense is more than 1. The ¢,n,E EW~ following formula explains the idea. where EWi = {ew I si ~ synset(ew) } 1 P ( si I ewj ) -~ -- Hi(s,) = max support( s,, ew~) - 1 ~'~, (n-1)+a k,=l nj where si ~ synset(ewj), nj = where EWi = (ewl s, ~ synset(ew)} Isyr et( w,)l In this formula, Hi(s) is a heuristic score of In this formula, n is the number of synsets of synset s, s is a candidate synset, ew is a the translation e~t~. translation into English, n is the number of 2.3 Heuristic 3: Sense Ordering translations and synset(ew) is the set of synsets (Gale et al., 1992) reports that word sense of the translation ew. So Ew becomes the set of disambiguation would be at least 75% correct if a translations which have the synset s r. The system assigns the most frequently occurring parameter tx controls the relative contribution of sense. (Miller et al., 1994) found that automatic candidate synsets in different number of translations: as the value of a increases, the I We use English WordNet version 1.6 - L 143 assignment of polysemous words in Brown If two Korean words have an IS-A relation, Corpus to senses in WordNet was 58% correct their translations in English should also with a heuristic of most frequently occurring have an IS-A relation. sense. We adopt these previous results to develop sense ordering heuristic. Korean word English word WordNet The sense ordering heuristic provides the maximum score to the most frequently used sense of a Ixanslation. The following formula explains the heuristic. H3(sO = max SO(s,,ew) ewEEW, where EW, = {ew I si ~ synset(ew) } Figure 3: IS-A relation ot SO(s,,ew) x~ Figure 3 explains IS-A relation heuristic. In figure 3, hkw is a hypemym of a Korean word kw where si ~ synset(ew) and hew is a translation of hkw and ew is a ^ synset(ew) is sorted by frequency translation of kw. ^ s, is the x- th synset in synset(ew) This heuristic assigns score 1 to the synsets which satisfy the above assumption according to the following formula: In this formula, x refers to the sense order of s in synset(ew): x is 1 when s, is the most H,(s,) = max 1R(si, ew) frequently used sense of ew. The information ew~EW, about the sense order of synsets of an English where EWi = {ewl si ~ synset(ew) } word was extracted from the WordNet. 0.8 IR(s,,ew)={; otherwisefflsa(s"si) prob 0.7 where si ~ synset(ew) , sj ~ synset(hew) 0.$ o.5 In this formula, lsA(s,,s 2) returns true if s, is a 0.4 kind of s 2. 0.3 2.5 Heuristic 5: Word Match This heuristic assumes that related concepts 0.2 i O will be expressed using the same content words. o.1 I Given two definitions - that of the bilingual O. #"- " "tl. A A = dictionary and that of the WordNet - this ! 2 ] 4 5 $ 7 8 9 ordm" heuristic computes the total amount of shared content words. Figure 2: Sense distribution in SemCor Hs(si) = max WM (si,ew) The value a=0.705 and fl=2.2 was acquired from a regression of Figure 2 semcor corpus2 where EW~ = (ewl s~ ~ synset(ew) } data distribution. WM (si, ew) = sim(X,Yi) 2.4 Heuristic 4: IS-A relation sim(X,Y) = IX n YI This heuristic is based on the following facts: Ix rl In this formula, X is the set of content words in 2 semcor is a sense tagged corpus from part of English examples of bilingual dictionary and Y is Brown corpus. 144 the set of content words of definition and example of the synset s, in WordNet. 2.6 Heuristic 6: Cooccurrence //~ Heur,st,c3 ~ ~K This heuristic uses cooccurrence measure ~ Heuristic4 ~ ('~'~"~ acquired from the sense tagged Korean definition sentences of bilingual dictionary. To build sense tagged corpus, we use the definition \ 1 x -H Ig2 l I Oeoi oo sentences which have monosemous translation [ Classfflcatzon I ~ -~" [ tree learning in bilingual dictionary. And we uses the 25 semantic tags of WordNet as sense tag : Figure 4: Training phase n6(s,) = max p(t, I x) xeDef Heuristic 2 with p =, - Z(l_a)/2 × ~'(1~ ~) Heuristic 3 n L=nklng or p(ti l x) = Freq(ti, x) HeunsUc 4 Discarding Freq(x) Heunstlc 5 Heunst=c 6 In this formula, Defis the set of content words of a Korean definition sentence, t is a semantic Figure 5: Mapping phase tag corresponding to the synset s and n refers to Freq(x).