The high-dimensionality of lexical features in parsing can be memory consuming and cause over-fitting problems. We propose a general framework to replace all lexical feature templates by low-dimensional features induced from word embeddings. Applied to a near state-of-the-art dependency parser (Huang et al., 2012), our method improves the baseline, performs better than using cluster bit string features, and outperforms a recent neural network based parser. A further analysis shows that our framework has the effect hypothesized by Andreas and Klein (2014), namely (i) connecting unseen words to known ones, and (ii) encouraging common behaviors among invocabulary words.
|出版ステータス||Published - 2015 1月 1|
|イベント||29th Pacific Asia Conference on Language, Information and Computation, PACLIC 2015 - Shanghai, China|
継続期間: 2015 10月 30 → 2015 11月 1
|Other||29th Pacific Asia Conference on Language, Information and Computation, PACLIC 2015|
|Period||15/10/30 → 15/11/1|
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