Statistical Grammar Models and Lexicon Acquisition 12.1 Introduction

This paper presents a framework for developing and training statistical grammar models for the acquisition of lexicon information. Util-ising a robust parsing environment and mathematically well-deened unsupervised training methods, the framework enables us to induce lexicon information from text corpora. Particular strengths of the approach concern (i) the fact that no extensive manual work is required to set up the framework, and (ii) that the framework is applicable to any desired language. It has already been applied to English and Manual work within the framework is reduced to a minimum, since the necessary grammars need not go into detailed structures for the relevant grammar aspects to be trained suuciently. The automatic training process utilises a shallow parser embedded in the mathematically well-deened Expectation-Maximisation algorithm. The training approach enforces the lexicalised parameters in the statistical grammar to obtain linguistic reliability. A basic assumption thereby expects that the linguistically correct analyses of text correspond to those analyses which 59 Linguistic Form and its Computation.

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