Some Novel Applications of Explanation-Based Learning to Parsing Lexicalized Tree-Adjoining Grammars

Some Novel Applications of Explanation-Based Learning to Parsing Lexicalized Tree-Adjoining Grammars" B. Srinivas and Aravind K. Joshi Department of Computer and Information Science University of Pennsylvania Philadelphia, PA 19104, USA {srini, joshi} @linc.cis.upenn.edu Abstract In this paper we present some novel ap- plications of Explanation-Based Learning (EBL) technique to parsing Lexicalized Tree-Adjoining grammars. The novel as- pects are (a) immediate generalization of parses in the training set, (b) generaliza- tion over recursive structures and (c) rep- resentation of generalized parses as Finite State Transducers. A highly impoverished parser called a "stapler" has also been in- troduced. We present experimental results using EBL for different corpora and archi- tectures to show the effectiveness of our ap- proach. 1 Introduction In this paper we present some novel applications of the so-called Explanation-Based Learning technique (EBL) to parsing Lexicalized Tree-Adjoining gram- mars (LTAG). EBL techniques were originally intro- duced in the AI literature by (Mitchell et al., 1986; Minton, 1988; van Harmelen and Bundy, 1988). The main idea of EBL is to keep track of problems solved in the past and to replay those solutions to solve new but somewhat similar problems in the future. Although put in these general terms the approach sounds attractive, it is by no means clear that EBL will actually improve the performance of the system using it, an aspect which is of great interest to us here. Rayner (1988) was the first to investigate this technique in the context of natural language pars- ing. Seen as an EBL problem, the parse of a sin- gle sentence represents an explanation of why the sentence is a part of the language defined by the grammar. Parsing new sentences amounts to find- ing analogous explanations from the training sen- tences. As a special case of EBL, Samuelsson and *This work was partiaJly supported by ARC) grant DAAL03-89-0031, ARPA grant N00014-90-J-1863, NSF STC grsmt DIR-8920230, and Ben Franklin Partnership Program (PA) gremt 93S.3078C-6 Rayner (1991) specialize a grammar for the ATIS domain by storing chunks of the parse trees present in a treebank of parsed examples. The idea is to reparse the training examples by letting the parse tree drive the rule expansion process and halting the expansion of a specialized rule if the current node meets a 'tree-cutting' criteria. However, the prob- lem of specifying an optimal 'tree-cutting' criteria was not addressed in this work. Samuelsson (1994) used the information-theoretic measure of entropy to derive the appropriate sized tree chunks automati- cally. Neumann (1994) also attempts to specialize a grammar given a training corpus of parsed exam- pies by generalizing the parse for each sentence and storing the generalized phrasal derivations under a suitable index. Although our work can be considered to be in this general direction, it is distinct in that it ex- ploits some of the key properties of LTAG to (a) achieve an immediate generalization of parses in the training set of sentences, (b) achieve an additional level of generalization of the parses in the training set, …

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