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            <namePart type="given">Yusuke</namePart>
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    <abstract>This paper presents a joint model for morphological and dependency analysis based on automatically acquired lexical knowledge. This model takes advantage of rich lexical knowledge to simultaneously resolve word segmentation, POS, and dependency ambiguities. In our experiments on Japanese, we show the effectiveness of our joint model over conventional pipeline models.</abstract>
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%0 Conference Proceedings
%T Automatically Acquired Lexical Knowledge Improves Japanese Joint Morphological and Dependency Analysis
%A Kawahara, Daisuke
%A Hayashibe, Yuta
%A Morita, Hajime
%A Kurohashi, Sadao
%Y Miyao, Yusuke
%Y Sagae, Kenji
%S Proceedings of the 15th International Conference on Parsing Technologies
%D 2017
%8 September
%I Association for Computational Linguistics
%C Pisa, Italy
%F kawahara-etal-2017-automatically
%X This paper presents a joint model for morphological and dependency analysis based on automatically acquired lexical knowledge. This model takes advantage of rich lexical knowledge to simultaneously resolve word segmentation, POS, and dependency ambiguities. In our experiments on Japanese, we show the effectiveness of our joint model over conventional pipeline models.
%U https://aclanthology.org/W17-6301
%P 1-10

Markdown (Informal)

[Automatically Acquired Lexical Knowledge Improves Japanese Joint Morphological and Dependency Analysis](https://aclanthology.org/W17-6301) (Kawahara et al., IWPT 2017)

• Automatically Acquired Lexical Knowledge Improves Japanese Joint Morphological and Dependency Analysis (Kawahara et al., IWPT 2017)

ACL

• Daisuke Kawahara, Yuta Hayashibe, Hajime Morita, and Sadao Kurohashi. 2017. Automatically Acquired Lexical Knowledge Improves Japanese Joint Morphological and Dependency Analysis. In Proceedings of the 15th International Conference on Parsing Technologies, pages 1–10, Pisa, Italy. Association for Computational Linguistics.