Paper: Discriminative Classifiers For Deterministic Dependency Parsing

ACL ID P06-2041
Title Discriminative Classifiers For Deterministic Dependency Parsing
Venue Annual Meeting of the Association of Computational Linguistics
Session Poster Session
Year 2006
Authors

Deterministic parsing guided by treebank- induced classifiers has emerged as a simple and efficient alternative to more complex models for data-driven parsing. We present a systematic comparison of memory-based learning (MBL) and sup- port vector machines (SVM) for inducing classifiers for deterministic dependency parsing, using data from Chinese, English and Swedish, together with a variety of different feature models. The comparison shows that SVM gives higher accuracy for richly articulated feature models across all languages, albeit with considerably longer training times. The results also confirm that classifier-based deterministic parsing can achieve parsing accuracy very close to the best results reported for more complex parsing models.