Multilingual Deep Lexical Acquisition for HPSGs via Supertagging

Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (EMNLP 2006), pages 164–171, Sydney, July 2006. c⃝2006 Association for Computational Linguistics Multilingual Deep Lexical Acquisition for HPSGs via Supertagging Phil Blunsom and Timothy Baldwin Computer Science and Software Engineering University of Melbourne, Victoria 3010 Australia {pcbl,tim}@csse.unimelb.edu.au Abstract 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. 1 Introduction Over recent years, there has been a resurgence of interest in the use of precision grammars in NLP tasks, due to advances in parsing algorithm de- velopment, grammar development tools and raw computational power (Oepen et al., 2002b). Pre- cision grammars are defined as implemented grammars of natural language which capture fine- grained linguistic distinctions, and are generative in the sense of distinguishing between grammat- ical and ungrammatical inputs (or at least have some in-built notion of linguistic “markedness”). Additional characteristics of precision grammars are that they are frequently bidirectional, and out- put a rich semantic abstraction for each span- ning parse of the input string. Examples include DELPH-IN grammars such as the English Resource Grammar (Flickinger, 2002; Uszkoreit, 2002), the various PARGRAM grammars (Butt et al., 1999), and the Edinburgh CCG parser (Bos et al., 2004). Due to their linguistic complexity, precision grammars are generally hand-constructed and thus restricted in size and coverage. Attempts to (semi-)automate the process of expanding the cov- erage of precision grammars have focused on ei- ther: (a) constructional coverage, e.g. in the form of error mining for constructional expansion (van Noord, 2004; Zhang and Kordoni, 2006), or relax- ation of lexico-grammatical constraints to support partial and/or robust parsing (Riezler et al., 2002); or (b) lexical coverage, e.g. in bootstrapping from a pre-existing grammar and lexicon to learn new lexical items (Baldwin, 2005a). Our particular in- terest in this paper is in the latter of these two, that is the development of methods for automati- cally expanding the lexical coverage of an existing precision grammar, or more broadly deep lexical acquisition (DLA hereafter). In this, we follow Baldwin (2005a) in assuming a semi-mature pre- cision grammar with a fixed inventory of lexical types, based on which we learn new lexical items. For the purposes of this paper, we focus specif- ically on supertagging as the mechanism for hy- pothesising new lexical items. Supertagging can be defined as the process of applying a sequential tagger to the task of predict- ing the lexical type(s) associated with each word in an input …

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