Structural Matching of Parallel Texts

STRUCTURAL MATCHING OF PARALLEL TEXTS Yuji Matsulnoto Graduate School of Information Science Advanced Institute of Science and Technology, Nara Takayanaa-cho, Ikoma-shi, Na.ra 630-01 Japan matsu@is.a ist-na ra.ac.jp Hiroyuki Ishimoto Takehito Utsuro Department of Electrical Engineering Kyoto University Sakyo-ku, Kyoto 606 Japan {ishimoto, utsuro} @pine.kuee.kyoto-u.ac.jp Abstract This paper describes a method for finding struc- rural matching between parallel sentences of two languages, (such as Japanese and English). Par- allel sentences are analyzed based on unification grammars, and structural matching is performed by making use of a similarity measure of word pairs in the two languages. Syntactic ambiguities are re- solved simultaneously in the matching process. The results serve as a. useful source for extracting lin- guistic a.nd lexical knowledge. INTRODUCTION Bilingual (or parallel) texts are useful resources for acquisition of linguistic knowledge as well as for ap- plications such as machine translation. Intensive research has been done for aligning bilingual texts at the sentence level using statistical teclmiques by measuring sentence lengths in words or in charac- ters (Brown 91), (Gale 91a). Those works are quite successful in that far more than 90% of sentences in bilingual corpora, are a.ligned correctly. Although such parallel texts are shown to be use- ful in real applications such as machine translation (Brown 90) and word sense disambiguatioll (Daga.n 91), structured bilingual sentences are undoubtedly more informative and important for filture natural language researches. Structured bilingual or multi- lingual corpora, serve a.s richer sources for extract- ing linguistic knowledge (Kaji 92), (Klavans 90), (Sadler 91), (Utsuro 92). Phrase level or word level alignment has also been done by several researchers. The Textual Knowledge Bank Project (Sadler 91) is building lnonolingual and multilingual text bases structured by linking the elements with grammatical (depen- dency), referential, and bilingual relations. (Karl 92) reports a method to obtain phrase level corre- spondence of parallel texts by coupling phrases of two languages obtained in CKY parsing processes. This paper presents another method to obtain structural matching of bilingual texts. Sentences in both languages are parsed to produce (disjunctive) feature structures, from which dependency struc- tures are extracted. Ambiguities are represented as disjunction. Then, the two structures are matched to establish a one-to-one correspondence between their substructures. The result of the match is ob- tained as a set of pairs of minimal corresponding substructures of the dependency structures. Exam- ples of the results are shown in Figures 1, 2 and 3. A dependency structure is represented as a tree, in which ambiguity is specified by a disjunctive node (OR. node). Circles in the figure show substruc- tures and bidirectional arrows show corresponding substructures. Our technique and the results are different from those of other lnethods mentioned above. (Kaji 92) identifies corresponding phrases and ahns at pro- ducing tra.nslation templates by abstracting those corresponding phrases. In the Bilingua.l Knowledge Bank (Sadler 91), the correspondence is shown by 23 links between words in two sentences, equating two whole subtrees headed by the words. We prefer the Ininimal substructure correspondence and the relationship between …

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