Supervised Grammar Induction using Training Data with Limited Constituent Information

Supervised Grammar Induction using Training Data with Limited Constituent Information * Rebecca Hwa Division of Engineering and Applied Sciences Harvard University Cambridge, MA 02138 USA rebecca@eecs.harvard.edu Abstract Corpus-based grammar induction generally re- lies on hand-parsed training data to learn the structure of the language. Unfortunately, the cost of building large annotated corpora is pro- hibitively expensive. This work aims to improve the induction strategy when there are few labels in the training data. We show that the most in- formative linguistic constituents are the higher nodes in the parse trees, typically denoting com- plex noun phrases and sentential clauses. They account for only 20% of all constituents. For in- ducing grammars from sparsely labeled training data (e.g., only higher-level constituent labels), we propose an adaptation strategy, which pro- duces grammars that parse almost as well as grammars induced from fully labeled corpora. Our results suggest that for a partial parser to replace human annotators, it must be able to automatically extract higher-level constituents rather than base noun phrases. 1 Introduction The availability of large hand-parsed corpora such as the Penn Treebank Project has made high-quality statistical parsers possible. How- ever, the parsers risk becoming too tailored to these labeled training data that they cannot re- liably process sentences from an arbitrary do- main. Thus, while a parser trained on the • Wall Street Journal corpus can fairly accurately parse a new Wall Street Journal article, it may not perform as well on a New Yorker article. To parse sentences from a new domain, one would normally directly induce a new grammar * This material is based upon work supported by the Na- tional Science Foundation under Grant No. IRI 9712068. We thank Stuart Shieber for his guidance, and Lillian Lee, Ric Crabbe, and the three anonymous reviewers for their comments on the paper. from that domain, in which the training pro- cess would require hand-parsed sentences from the new domain. Because parsing a large cor- pus by hand is a labor-intensive task, it would be beneficial to minimize the number of labels needed to induce the new grammar. We propose to adapt a grammar already trained on an old domain to the new domain. Adaptation can exploit the structural similar- ity between the two domains so that fewer la- beled data might be needed to update the gram- mar to reflect the structure of the new domain. This paper presents a quantitative study com- paring direct induction and adaptation under different training conditions. Our goal is to un- derstand the effect of the amounts and types of labeled data on the training process for both induction strategies. For example, how much training data need to be hand-labeled? Must the parse trees for each sentence be fully spec- ified? Are some linguistic constituents in the parse more informative than others? To answer these questions, we have performed experiments that compare the parsing quali- ties of grammars induced under different train- ing conditions using both adaptation and di- rect induction. We vary …

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