Automatic Grammar Induction and Parsing Free Text: A Transformation-Based Approach

Automatic Grammar Induction and Parsing Free Text: A Transformation-Based Approach Eric Brill * Department of Computer and Information Science University of Pennsylvania brill @ unagi.cis.upenn.edu ABSTRACT In this paper we describe a new technique for parsing free text: a transformational grammar I is automatically learned that is capable of accurately parsing text into binary-branching syntactic trees with nonterminals unlabelled. The algorithm works by beginning in a very naive state of knowledge about phrase structure. By repeatedly comparing the results of bracketing in the current state to proper bracketing provided in the training corpus, the system learns a set of simple structural transformations that can be applied to reduce error. After describing the algorithm, we present results and compare these results to other recent results in automatic grammar induction. 1. INTRODUCTION There has been a great deal of interest of late in the automatic induction of natural language grammar. Given the difficulty inherent in manually building a robust parser, along with the availability of large amounts of training material, automatic grammar induction seems like a path worth pursuing. A number of systems have been built which can be trained au- tomatically to bracket text into syntactic constituents. In [ 10] mutual information statistics are extracted from a corpus of text and this information is then used to parse new text. [13] defines a function to score the quality of parse trees, and then uses simulated annealing to heuristically explore the entire space of possible parses for a given sentence. In [3], distri- butional analysis techniques are applied to a large corpus to learn a context-free grammar. The most promising results to date have been based on the inside-outside algorithm (i-o algorithm), which can be used to train stochastic context-free grammars. The i-o algorithm is an extension of the finite-state based Hidden Markov Model (by [1]), which has been applied successfully in many areas, including speech recognition and part of speech tagging. A number of recent papers have explored the potential of using the i-o algorithm to automatically learn a grammar [9, 15, 12, 6, 7, 14]. Below, we describe a new technique for grammar induction. 2 *The author would like to thank Mark Liberman, Meiting Lu, David Magerman, Mitch Marcus, Rich Pito, Giorgio Satta, Yves Schabes and Tom Veatch. This work was supported by DARPA and AFOSR jointly under grant No. AFOSR-90-0066, and by A'RO grant No. DAAL 03-89-C0031 PRI. INot in the traditional sense of the term. 2A similar method has been applied effectively in part of speech tagging; The algorithm works by beginning in a very naive state of knowledge about phrase structure. By repeatedly comparing the results of parsing in the current state to the proper phrase structure for each sentence in the training corpus, the system learns a set of ordered transformations which can be applied to reduce parsing error. We believe this technique has ad- vantages over other methods of phrase structure induction. Some of the advantages include: the system is very simple, it requires only …

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