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)

Deterministic dependency parsing is a robust and efficient approach to syntactic parsing of unrestricted natural language text. In this paper, we analyze its potential for incremental processing and conclude that strict incrementality is not achievable within this framework. However, we also show that it is possible to minimize the number of structures that require nonincremental processing by choosing an optimal parsing algorithm. This claim is substantiated with experimental evidence showing that the algorithm achieves incremental parsing for 68.9% of the input when tested on a random sample of Swedish text. When restricted to sentences that are accepted by the parser, the degree of incrementality increases to 87.9%. 1

We present interpretation-based processing—a theory of sentence processing that builds a syntactic and a semantic representation for a sentence and assigns an interpretation to the sentence as soon as possible. That interpretation can further participate in comprehension and in lexical processing and is vital for relating the sentence to the prior discourse. Our theory offers a unified account of the processing of literal sentences, metaphoric sentences, and sentences containing semantic illusions. It also explains how text can prime lexical access. We show that word literality is a matter of degree and that the speed and quality of comprehension depend both on how similar words are to their antecedents in the preceding text and how salient the sentence is with respect to the preceding text. Interpretation-based processing also reconciles superficially contradictory findings about the difference in processing times for metaphors and literals. The theory has been implemented in ACT-R [Anderson and Lebiere, The

The paper describes a parser for Categorial Grammar which provides fully word by word incremental interpretation. The parser does not require fragments of sen- tences to form constituents, and thereby avoids problems of spurious ambiguity. The paper includes a brief discussion of the relationship between basic Catego- rial Grammar and other formalisms such as HPSG, Dependency Grammar and the Lambek Calculus. It also includes a discussion of some of the issues which arise when parsing lexicalised grammars, and the possibilities for using statistical techniques for tuning to particular lan- guages.

Data-Oriented Parsing (DOP) is a probabilistic performance approach to parsing natural language. Several DOP models have been proposed since it was introduced by Scha (1990), achieving promising results. One important feature of these models is the probability estimation procedure. Two major estimators have been put forward: Bod (1993) uses a relative frequency estimator; Bonnema (1999) adds a rescaling factor to correct for tree size effects. Both estimators, however, present biases. Moreover, Bod's estimator has been shown to be inconsistent (Johnson, 2002), meaning that the probability estimates hypothesized by the model do not approach the true probabilities that generated the data as the sample size grows. In this thesis, we implement a new estimation procedure that tackles the shortcomings of the two previous methods. The main idea is to treat derivation events not as disjoint, but as interrelated in a hierarchical cascade of parse tree derivations. We show that this new estimator -- called the Back-Off DOP (BO-DOP) estimator -- outperforms both previous models. We tested it on the OVIS treebank, a Dutch language, speech-based system, and report error reductions of up to 11.4% and 15% when compared to, respectively, Bod's and Bonnema's estimators.

In this paper we develop novel algorithmic ideas for building a natural language parser grounded upon the hypothesis of incrementality. Although widely accepted and experimentally supported under a cognitive perspective as a model of the human parser, the incrementality assumption has never been exploited for building automatic parsers of unconstrained real texts. The essentials of the hypothesis are that words are processed in a left-to-right fashion, and the syntactic structure is kept totally connected at each step. Our proposal relies on a machine learning technique for predicting the correctness of partial syntactic structures that are built during the parsing process. A recursive neural network architecture is employed for computing predictions after a training phase on examples drawn from a corpus of parsed sentences, the Penn Treebank. Our results indicate the viability of the approach and lay out the premises for a novel generation of algorithms for natural language processing which more closely model human parsing. These algorithms may prove very useful in the development of ecient parsers. 1

One of the central problems in the study of human language processing is ambiguity resolution: How do people resolve the extremely pervasive ambiguity of the language they encounter? One possible answer to this question is suggested by experiencebased models, which claim that people typically resolve ambiguities in a way which has been successful in the past. In order to determine the course of action that has been "successful in the past" when faced with some ambiguity, it is necessary to generalise over past experience. In this paper, we will present a computational experience-based model, which learns to generalise over linguistic experience from exposure to syntactic structures in a corpus. The model is a hybrid system, which uses symbolic grammars to build and represent syntactic structures, and neural networks to rank these structures on the basis of its experience. We use a dynamic grammar, which provides a very tight correspondence between grammatical derivations and incremental processing, and recursive neural networks, which are able to deal with the complex hierarchical structures produced by the grammar. We demonstrate that the model reproduces a number of the structural preferences found in the experimental psycholinguistics literature, and also performs well on unrestricted text.

Why should computers interpret language incrementally? In recent years psycholinguistic evidence for incremental interpretation has become more and more compelling, suggesting that humans perform semantic interpretation before constituent boundaries, possibly word by word. However, possible computational applications have received less attention. In this paper we consider various potential applications, in particular graphical interaction and dialogue. We then review the theoretical and computational tools available for mapping from fragments of sentences to fully scoped semantic representations. Finally, we tease apart the relationship between dynamic semantics and incremental interpretation.

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Abstract. This paper presents a novel method for wide coverage parsing using an incremental strategy, which is psycholinguistically motivated. A recursive neural network is trained on treebank data to learn first pass attachments, and is employed as a heuristic for guidingparsingdecision. The parser is lexically blind and uses beam search to explore the space of plausible partial parses and returns the full analysis havinghighest probability. Results are based on preliminary tests on the WSJ section of the Penn treebank and suggest that our incremental strategy is a computationally viable approach to parsing. 1