Automatic Selection of Predicates for Common Sense Knowledge Expression長岡技術科学大学 自然言語処理研究室
Ai Makabi, Hiroshi Matsumoto and Kazuhide Yamamoto. Automatic Selection of Predicates for Common Sense Knowledge Expression. Proceedings of the Conference of the Pacific Association for Computational Linguistics (PACLING 2013), no page numbers (2013.9)
Automatic Selection of Predicates for Common Sense Knowledge Expression長岡技術科学大学 自然言語処理研究室
Ai Makabi, Hiroshi Matsumoto and Kazuhide Yamamoto. Automatic Selection of Predicates for Common Sense Knowledge Expression. Proceedings of the Conference of the Pacific Association for Computational Linguistics (PACLING 2013), no page numbers (2013.9)
Developing User-friendly and Customizable Text Analyzer長岡技術科学大学 自然言語処理研究室
Yuki Miyanishi and Kazuhide Yamamoto. Developing User-friendly and Customizable Text Analyzer. The International Conference on Practical Linguistics of Japanese (ICPLJ8), pp.172-173 (2014.3)
A Comparison of Unsuperviesed Bilingual Term Extraction Methods Using Phrase ...長岡技術科学大学 自然言語処理研究室
The document presents a comparison of unsupervised bilingual term extraction methods using phrase tables. It develops unsupervised methods for extracting bilingual terms from phrase tables and compares their performance. Three statistical measures - ScoreF, ScoreL, and ScoreC - are used to extract terms and eliminate incorrect pairs. Experiments apply the methods to a Japanese-English parallel corpus and evaluate the extracted terms. Combining the measures performs best, as each method extracts different types of terms.
Developing User-friendly and Customizable Text Analyzer長岡技術科学大学 自然言語処理研究室
Yuki Miyanishi and Kazuhide Yamamoto. Developing User-friendly and Customizable Text Analyzer. The International Conference on Practical Linguistics of Japanese (ICPLJ8), pp.172-173 (2014.3)
A Comparison of Unsuperviesed Bilingual Term Extraction Methods Using Phrase ...長岡技術科学大学 自然言語処理研究室
The document presents a comparison of unsupervised bilingual term extraction methods using phrase tables. It develops unsupervised methods for extracting bilingual terms from phrase tables and compares their performance. Three statistical measures - ScoreF, ScoreL, and ScoreC - are used to extract terms and eliminate incorrect pairs. Experiments apply the methods to a Japanese-English parallel corpus and evaluate the extracted terms. Combining the measures performs best, as each method extracts different types of terms.