tailieunhanh - Báo cáo khoa học: "Discriminative Classifiers for Deterministic Dependency Parsing"

Deterministic parsing guided by treebankinduced 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 support 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. . | Discriminative Classifiers for Deterministic Dependency Parsing Johan Hall Vaxjo University j ni@ Joakim Nivre Vaxjo University and Uppsala University nivre@ Jens Nilsson Vaxjo University j ha@ Abstract 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 support 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. 1 Introduction Mainstream approaches in statistical parsing are based on nondeterministic parsing techniques usually employing some kind of dynamic programming in combination with generative probabilistic models that provide an n-best ranking of the set of candidate analyses derived by the parser Collins 1997 Collins 1999 Charniak 2000 . These parsers can be enhanced by using a discriminative model which reranks the analyses output by the parser Johnson et al. 1999 Collins and Duffy 2005 Charniak and Johnson 2005 . Alternatively discriminative models can be used to search the complete space of possible parses Taskar et al. 2004 McDonald et al. 2005 . A radically different approach is to perform disambiguation deterministically using a greedy parsing algorithm that approximates a globally optimal solution by making a sequence of locally optimal choices guided by a classifier trained on gold standard derivations from a treebank. This methodology has emerged as an alternative to more complex models especially