this article can compute solutions to all four of these problems in a single flamework, with a number of additional advantages over previously presented isolated solutions

sues in MT and the stands on these issues taken in the KBMT approach. The most important is the issue of transfer versus interlingua. In a nutshell, the question is: can analyzers and generators for the various languages in an MT system share the same (interlingual) representation, or must representations vary, with transfer modules being responsible for converting from the source language representation to the target language representation ? I think the authors are correct in concluding that this controversy really boils down to the question of how much semantic analysis is performed in an MT system. The more semantic analysis, the more like an interlingua the representation is likely to be. Aside from this point, though, ! did not find the discussion to be particu- 207 Computational Linguistics Volume 19, Number 1 larly original; many of the same points have been raised countless times before, dating as far back as Bar-Hillel's 1960 article discussing the feasibility of fully automa

Automating the construction of semantic grammars is a difficult and interesting problem for machine learning. This paper shows how the semantic-grammar acquisition problem can be viewed as the learning of search-control heuristics in a logic program. Appropriate control rules are learned using a new first-order induction algorithm that automatically invents useful syntactic and semantic categories. Empirical results show that the learned parsers generalize well to novel sentences and out-perform previous approaches based on connectionist techniques. Introduction Designing computer systems to "understand" natural language input is a difficult task. The laboriously hand-crafted computational grammars supporting natural language applications are often inefficient, incomplete and ambiguous. The difficulty in constructing adequate grammars is an example of the "knowledge acquisition bottleneck" which has motivated much research in machine learning. While numerous researchers have studied ...

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Packrat parsing is a novel technique for implementing parsers in a lazy functional programming language. A packrat parser provides the power and flexibility of top-down parsing with backtracking and unlimited lookahead, but nevertheless guarantees linear parse time. Any language defined by an LL(k) or LR(k) grammar can be recognized by a packrat parser, in addition to many languages that conventional linear-time algorithms do not support. This additional power simplifies the handling of common syntactic idioms such as the widespread but troublesome longest-match rule, enables the use of sophisticated disambiguation strategies such as syntactic and semantic predicates, provides better grammar composition properties, and allows lexical analysis to be integrated seamlessly into parsing. Yet despite its power, packrat parsing shares the same simplicity and elegance as recursive descent parsing; in fact converting a backtracking recursive descent parser into a linear-time packrat parser often involves only a fairly straightforward structural change. This paper describes packrat parsing informally with emphasis on its use in practical applications, and explores its advantages and disadvantages with respect to the more conventional alternatives.

The thesis describes novel techniques and algorithms for the practical parsing of realistic Natural Language (NL) texts with a wide-coverage unification-based grammar of English. The thesis tackles two of the major problems in this area: firstly, the fact that parsing realistic inputs with such grammars can be computationally very expensive, and secondly, the observation that many analyses are often assigned to an input, only one of which usually forms the basis of the correct interpretation. The thesis starts by presenting a new unification algorithm, justifies why it is well-suited to practical NL parsing, and describes a bottom-up active chart parser which employs this unification algorithm together with several other novel processing and optimisation techniques. Empirical results demonstrate that an implementation of this parser has significantly better practical

this paper. Thanks are also due to Steve Pulman, Ewan Klein, David Beaver and Guy Barry for discussion during the early stages of the work, and to other members of the University of Edinburgh Centre for Cognitive Science and the University of Cambridge Computer Laboratory. The research was supported by the British Science and Engineering Research Council (Research Fellowship B/90/ITF/288, and Research Grant RR30718)

The analysis of spoken language is widely considered to be a more challenging task than the analysis of written text. All of the difficulties of written language can generally be found in spoken language as well. Parsing spontaneous speech must, however, also deal with problems such as speech disfluencies, the looser notion of grammaticality, and the lack of clearly marked sentence boundaries. The contamination of the input with errors of a speech recognizer can further exacerbate these problems. Most natural language parsing algorithms are designed to analyze "clean" grammatical input. Because they reject any input which is found to be ungrammatical in even the slightest way, such parsers are unsuitable for parsing spontaneous speech, where completely grammatical input is the exception more than the rule. This thesis describes GLR*, a parsing system based on Tomita's Generalized LR parsing algorithm, that was designed to be robust to two particular types of extra-grammaticality: noise...

This paper introduces a paradigm of context-dependent grammar (CDG) and an acquisition system that, through interactive teaching sessions, accumulates the CDG rules. The resulting context-sensitive rules are used by a stack-based, shift~reduce parser to compute unambiguous syntactic structures of sentences. The acquisition system and parser have been applied to the phrase structure and case analyses of 345 sentences, mainly from newswire stories, with 99 % accuracy. Extrapolation from our current grammar predicts that about 25 thousand CDG rule examples will be sufficient to train the system in phrase structure analysis of most news stories. Overall, this research concludes that CDG is a computationally and conceptually tractable approach for the construction of sentence grammar for large subsets of natural language text. 1.

This paper presents a new approach to the recognition of sentence structure by computer in human languages that have variable word order. In a sense, the algorithm is not new; there is good evidence that it was known 700 years ago (Covington 1984). But it has not been implemented on computers, and the modern implementations that are most like it fail to realize its crucial advantage for dealing with variable word order. 1 In fact, present-day parsing technology is so tied to the fixed word order of English that researchers in Germany and Japan customarily build parsers for English rather than their own languages. The new