A Corpus-Based Learning Technique for Building A Self-Extensible Parser

A Corpus-Based Learning Technique for Building A Self-Extensible Parser Rey-Long Liu and Von-Wun Soo Department of Computer Science National Tsing-Hua University HsinChu, Taiwan, 1LO.C. e-mail: sooQ<s.nthu.edu.tw Abstract IIuman intervention and/or training corpora tagged with various kinds of information were often assumed in many natural language acquisition models. This assumption is a major source of inconsistencies, er- rors, and inefficiency in learning. In this paper, we explore the extent to which a parser may extend it- self without relying on extra input from the outside world. A learning technique called SEP is proposed and attached to the parser. The input to SEP is raw sentences, while the output is the knowledge that is missing in the parser. Since parsers and raw sentences are commonly available and no human intervention is needed in learning, SEP could make fully automatic large-scale acquisition more feasible. Keywords: fully automatic natural language acqui- sition, self-extensible parser, corpus-based learning 1 Introduction It is commonly believed in many psycholinguistics studies [Pinker, 1984; Wexler & Culicover, 1980] that extra input (in addition to raw sentences) is necessary for human language learners. Most existing compu- tational natural language acquisition models also as- sumed various kinds of the extra input (e.g. semantic associations [Siskind, 1990; Webster & Marcus, 1989; Zernik, 1987] and syntactic structures [Berwick, 1985; Liu & Soo, 1992a] of input sentences) and human intervention (e.g. information interactively given by the trainer [tang & Ilirschman, 1988; Velardi et al., 1991]) during learning. The preparation of tile ex- tra input and human intervention may often cause inconsistencies, errors, and inefficiency in learning. It is often a bottle neck in scaling up natural language processing systems. Therefore, simple syntactic heuristics had been used to collect the extra input [Brent, 1993; Sekine, 1992]. However, the information that may be collect- ed by the simple heuristics is limited. More sophisti- cated processors such as taggers and parsers had also been tested in collecting the extra input [Webster & Marcus, 1989; Zernik, 1989]. ttowever, since learning was based on the input that may be successfully and unambiguously analyzed by the processors, upgrad- ing tile performance of the processors became a new bottle neck of upgrading the performance of learning. Furthermore, since the heuristics and proccssors are separated from the learning component, the learning component cannot know what knowledge is actually missing in the heuristics and processors. The learn- ing component might learn the knowledge that the processors already have. In this paper, we investigate the robust ways of ac- quiring parsing knowledge without requiring extra in- put and truman intervention. The input to the system is raw sentences, while the output is the knowledge that is missing before learning. The parser takes an active role in collecting extra information for learn- ing. Thus a parser could be self-extensible in the sense that it couhl automatically acquire what it ac- tually lacks without relying on extra information from the outside world. The parsing capability is improved through learning, and in turn, the learning capability is improved …

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