Acquisition of Word Translations Using Local Focus-Based Learning in Ainu-Japanese Parallel Corpora.
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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ABSTRACT: This paper presents POST-AL, the first part-of-speech tagger for Ainu language. The system uses a hand-crafted dictionary based on Ainu narratives “yukar”. The system provides three types of information: word/token, part of speech, and translation of the token (in Japanese). Evaluation on a training set provided positive results. The system could be useful in a great number of tasks related to the research on Ainu language, such as content analysis or translation, which till now have been done mostly manually.Expert Systems with Applications 10/2012; 39(14):11576–11582. DOI:10.1016/j.eswa.2012.04.031 · 2.24 Impact Factor