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) extracting llseftll word chunks (n-grmns) by word-level sorting and (2) constructing bilingua,l ('ollocations t)y combining the word-(;hunl(s a(-quired iu stag(' (1). We apply the method to a very ('hallenging text l)~tir: a stock market 1)ullet;in in Japanese and il;s abstract in En-glish. I)om;tin sl)ecific collocations are well captured ewm if they were not conta.ined in the dictionaric's of economic tel?IllS.
This paper describes an accurate and robust text alignment system for structurally different languages. Among structurally different languages such as Japanese and English, there is a limitation on the amount of word correspondences that can be statistically acquired. The proposed method makes use of two kinds of word correspondences in aligning bilingual texts. One is a bilingual dictionary of general use. The other is the word correspondences that are statistically acquired in the alignment process. Our method gradually determines sentence pairs (anchors) that correspond to each other by relaxing parameters. The method, by combining two kinds of word correspondences, achieves adequate word correspondences for complete alignment. As a result, texts of various length and of various genres in structurally different languages can be aligned with high precision. Experimental results show our system outperforms conventional methods for various kinds of Japanese-English texts.
This palu:r prose.his our work towards the mtlomatic acquisition of translatiort "t'ules from Jatmnese-l')nglish transhdion examples fo'r NTT"s ALT'-J/I'2 .machine translation system. We apply two lttat:hinc lca'tvti~ 9 ab.loritim~s : lIaussler's algm'ithm fro" h:mvvirtg internal disj'tmctive concept and (~'uirdan's I1)3 algm'ithm. l,;:Cl)evimental results show that our al)trroach yields r'uh'.s that (lI'('. highly a(:c'twale colnpalvd to l]tc l/lilly(tally crv.atcd r'ules.
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