Learning Bilingual Collocations by Word-Level Sorting

Learning Bilingual Collocations by Word-Level Sorting Masahiko Haruno Satoru Ikehara Takefumi Yamazaki NTT (',ommunicatiolt Sci('nce l,al)s. 1-2:156 Take Yokosuka-Shi Ka.nagawa. 2:18-03, ,la,l:)a,n haruno@n'c'ckb, nt-I;, j p ikehara@nttkb, rt'c-I;, j p yamazaki~nttkb, ntt. j p Abstract This paper I)roposes ;t new tnethod for learning bilingual colloca, tions from sentence-aligned paralM corpora. Our method COml)ris('s two steps: (1) ex- tracting llseftll word chunks (n-grmns) by word-level sorting and (2) construct- ing bilingua,l ('ollocations t)y combining the word-(;hunl(s a(-quired iu stag(' (1). We apply the method to a very ('hal- lenging text l)~tir: a stock market 1)ul- let;in in Japanese and il;s abstract in En-- glish. I)om;tin sl)ecific collocations are well captured ewm if they were not con- ta.ined in the dictionaric's of economic tel?IllS. 1 Introduction In the field of machitm translation, there is a, growing interest in corl)llS-I)ased al)l)roa.('hes (Sato and Nagao, 1990; l)a.gan mid (',hutch, 199d; Mat. sulnoto et M., 19.93; Kumano m,d ll imka.wa., 199d ; Smadja et al., 1996). The main motiw~.tion be- hind this is to well ha.ndle, domain specific ex- pressions. I~ach apl~licatiotl dom~dn has va.rious kinds of collocations ranging from word-level to sentence-level. ']'he correct use of these colloca- tions grea.l.ly inlluellcC's the qua.lity ofoutpttt texts. Ilexa.uso such detaih'd collocations ;~r~'~ <tillicult 1:o hand-conlpile, the automatic extra(:tion of bilin- gual collocations is needed. A number of studies haw> aJ.tetnpte(I to extract bilinguaJ collocations from paralM corpora. These studies c~m be classified into two directions. One is hased on the full parsing techniques. (Mat,- sumoto et a l., 1993) I)roposed a. method to find out phrase-lew'l correspondences, while resolving syn- tactic ambiguities a.t the same time. Their ninth- ()(Is determine t)hrase eorresl)ondences I)y using the phrase structures of I,he two hmgua,ges and ox- isting bilingual dict.iona.ries. Unfi)rl.unately tl,'se al)proaches are protnising only for (,he compara-- I, ively short sentences l, hat ca, I>e a,a]yze(I I>y ;t (',I+,:Y type l>arser. The other direction for extracting bilingual cob local.ions involw+:s statistics. (Fung, 1995) ac- quired bilingual word correspondences without sentetlce alignment. Although these methods ;|re rob/Is| ~HI(I aSSllllle rio illfOl'lll~ttiOll SOltrce~ their outputs are just word-word corresl)otMences. (Kupiec, 1993; Kumano and }lirakawa, 1!194) ex- tracted noun phr~me (NP) correspondences from aligned parallel corpora. |n (Kupiec, 1993), Nl's in English and French t;exts are+ first extracted by a. NP recoguizer. Their correspotldence prol> abilities arc then gradually relined by using an EM-like iteration algorithm. (t,~uma.no and Ili- rakawa, 1994) lirst extracted .Japanese N Ps in the S&III(? way, and comhined statistics with a bilin- gtta.l dictionary tbr MT 1o find out NP (-orrespon- dences. Although their apl)ro+tches a.t.ta.ined high accuracy for the+ task considered, the most cru- cial knowledge for MT is tnorc COml~lex corre- spOlldelices Sllch ~-LS NI'-VP corres[,Oll(teltces atHI senl.et,'e-hwe[ eorrespotldences. It seems di[[icutt I.o extend these statistical lllethods to ~t I)roa.(ler rmtge of collocations because they are specialized to N l's o1: sillglc" words. (Smmlj~t et al., 1996) proposed a generM method to extract a I)roader range of colloca.tions. They first extract English collocations using the …

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