Explanation-Based Learning of Partial-Parsers

This paper presents a method for learning efficient parsers of natural language. The method consists of an Explanation-Based Learning (EBL) algorithm for learning partialparsers, and a parsing algorithm which combines partial-parsers with existing "fullparsers ". The learned partial-parsers, implementable as Cascades of Finite State Transducers (CFSTs), recognize and combine constituents efficiently, prohibiting spurious overgeneration. The parsing algorithm combines a learned partial-parser with a given fullparser such that the role of the full-parser is limited to combining the constituents, recognized by the partial-parser, and to recognizing unrecognized portions of the input sentence. We exhibit encouraging empirical results using a pilot implementation: parse-space is reduced substantially with minimal loss of coverage. 1 Introduction Current work on natural language parsing is in large part directed towards eliminating overgeneration of grammars by employing stochastic models f..