Abstract
Center-embedding is difficult to process and is known as a rare syntactic construction across languages. In this paper we describe a method to incorporate this assumption into the grammar induction tasks by restricting the search space of a model to trees with limited center-embedding. The key idea is the tabulation of left-corner parsing, which captures the degree of center-embedding of a parse via its stack depth. We apply the technique to learning of famous generative model, the dependency model with valence (Klein and Manning, 2004). Cross-linguistic experiments on Universal Dependencies show that often our method boosts the performance from the baseline, and competes with the current state-of-the-art model in a number of languages.
Original language | English |
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Title of host publication | Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 33-43 |
Number of pages | 11 |
ISBN (Electronic) | 9781945626258 |
DOIs | |
Publication status | Published - 1 Jan 2016 |
Event | 2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016 - Austin, United States Duration: 1 Nov 2016 → 5 Nov 2016 |
Conference
Conference | 2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016 |
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Country | United States |
City | Austin |
Period | 1/11/16 → 5/11/16 |