We present a semi-supervised approach to improve dependency parsing accuracy by using bilexical statistics derived from auto-parsed data. The method is based on estimating the attachment potential of head-modifier words, by taking into account not only the head and modifier words themselves, but also the words surrounding the head and the modifier. When integrating the learned statistics as features in a graph-based parsing model, we observe nice improvements in accuracy when parsing various English datasets.
|Title of host publication||Conference Proceedings - EMNLP 2015|
|Subtitle of host publication||Conference on Empirical Methods in Natural Language Processing|
|Publisher||Association for Computational Linguistics (ACL)|
|Number of pages||6|
|State||Published - 2015|
|Event||Conference on Empirical Methods in Natural Language Processing, EMNLP 2015 - Lisbon, Portugal|
Duration: 17 Sep 2015 → 21 Sep 2015
|Name||Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing|
|Conference||Conference on Empirical Methods in Natural Language Processing, EMNLP 2015|
|Period||17/09/15 → 21/09/15|
Bibliographical notePublisher Copyright:
© 2015 Association for Computational Linguistics.