Abstract
This paper describes a new learning method for acquisition of word translations from small parallel corpora. Our proposed method, Local Focus-based Learning (LFL), efficiently acquires word translations and collocation templates by focusing on parts of sentences, not on entire sentences. Collocation templates have collocation information to acquire word translations from each sentence pair. This method is useful even when frequency of appearances of word translations is very low in sentence pairs. The LFL system described in this paper extracts Ainu-Japanese word translations from small Ainu-Japanese parallel corpora. The Ainu language is spoken by the Ainu ethnic group residing in northern Japan and Sakhalin. An evaluation experiment indicated that the recall was 57.4% and the precision was 72.0% to 546 kinds of nouns and verbs in 287 Ainu-Japanese sentence pairs even though the average frequency of appearances of the 546 kinds of nouns and verbs was 1.98.
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© 2004 Springer-Verlag Berlin Heidelberg
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Echizen-ya, H., Araki, K., Momouchi, Y., Tochinai, K. (2004). Acquisition of Word Translations Using Local Focus-Based Learning in Ainu-Japanese Parallel Corpora. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2004. Lecture Notes in Computer Science, vol 2945. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24630-5_36
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DOI: https://doi.org/10.1007/978-3-540-24630-5_36
Publisher Name: Springer, Berlin, Heidelberg
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