Paper: Multilingual Deep Lexical Acquisition For HPSGs Via Supertagging

ACL ID W06-1620
Title Multilingual Deep Lexical Acquisition For HPSGs Via Supertagging
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2006
Authors

We propose a conditional random field- based method for supertagging, and ap- ply it to the task of learning new lexi- cal items for HPSG-based precision gram- mars of English and Japanese. Us- ing a pseudo-likelihood approximation we are able to scale our model to hun- dreds of supertags and tens-of-thousands of training sentences. We show that it is possible to achieve start-of-the-art results for both languages using maxi- mally language-independent lexical fea- tures. Further, we explore the performance of the models at the type- and token-level, demonstrating their superior performance when compared to a unigram-based base- line and a transformation-based learning approach.