Ambiguity resolution is a central problem in language comprehension. Lexical and syntactic ambiguities are standardly assumed to involve different types of knowledge representations and be resolved by different mechanisms. An alternative account is provided in which both types of ambiguity derive from aspects of lexical representation and are resolved by the same processing mechanisms. Reinterpreting syntactic ambiguity resolution as a form of lexical ambiguity resolution obviates the need for special parsing principles to account for syntactic interpretation preferences, reconciles a number of apparently conflicting results concerning the roles of lexical and contextual information in sentence processing, explains differences among ambiguities in terms of ease of resolution, and provides a more unified account of language comprehension than was previously available. One of the principal goals for a theory of language compre- third section we consider processing issues: how information is hension is to explain how the reader or listener copes with a processed within the mental lexicon and how contextual inforpervasive ambiguity problem. Languages are structured at mation can influence processing. The central processing mechmultiple levels simultaneously, including lexical, phonological, anism we invoke is the constraint satisfaction process that has morphological, syntactic, and text or discourse levels. At any been realized in interactive-activation models (e.g., Elman &

This paper proposes a new theory of the relationship between the sentence processing mechanism and the available computational resources. This theory -- the Syntactic Prediction Locality Theory (SPLT) -- has two components: an integration cost component and a component for the memory cost associated with keeping track of obligatory syntactic requirements. Memory cost is

The problems of access -- retrieving linguistic structure from some mental grammar -- and disambiguation -- choosing among these structures to correctly parse ambiguous linguistic input -- are fundamental to language understanding. The literature abounds with psychological results on lexical access, the access of idioms, syntactic rule access, parsing preferences, syntactic disambiguation, and the processing of garden-path sentences. Unfortunately, it has been difficult to combine models which account for these results to build a general, uniform model of access and disambiguation at the lexical, idiomatic, and syntactic levels. For example psycholinguistic theories of lexical access and idiom access and parsing theories of syntactic rule access have almost no commonality in methodology or coverage of psycholinguistic data. This paper presents a single probabilistic algorithm which models both the access and disambiguation of linguistic knowledge. The algorithm is based on a parallel parser which ranks constructions for access, and interpretations for disambiguation, by their conditional probability. Low-ranked constructions and interpretations are pruned through beam-search; this pruning accounts, among other things, for the garden-path effect. I show that this motivated probabilistic treatment accounts for a wide variety of psycholinguistic results, arguing for a more uniform representation of linguistic knowledge and for the use of probabilisticallyenriched grammars and interpreters as models of human knowledge of and processing of language.

Introduction Despite considerable diversity among theories about how humans process language, there are a number of fundamental assumptions which are shared by most such theories. This consensus extends to the very basic question about what counts as a cognitive process. So although many cognitive scientists are fond of referring to the brain as a `mental organ' (e.g., Chomsky, 1975)---implying a similarity to other organs such as the liver or kidneys---it is also assumed that the brain is an organ with special properties which set it apart. Brains `carry out computation' (it is argued)

We present a detailed process theory of the moment-by-moment working-memory retrievals and associated control structure that subserve sentence comprehension. The theory is derived from the application of independently motivated principles of memory and cognitive skill to the specialized task of sentence parsing. The resulting theory construes sentence processing as a series of skilled associative memory retrievals modulated by similarity-based interference and fluctuating activation. The cognitive principles are formalized in computational form in the Adaptive Control of Thought–Rational (ACT–R) architecture, and our process model is realized in ACT–R. We present the results of 6 sets of simulations: 5 simulation sets provide quantitative accounts of the effects of length and structural interference on both unambiguous and garden-path structures. A final simulation set provides a graded taxonomy of double center embeddings ranging from relatively easy to extremely difficult. The explanation of center-embedding difficulty is a novel one that derives from the model’s complete reliance on discriminating retrieval cues in the absence of an explicit representation of serial order information. All fits were obtained with only 1 free scaling parameter fixed across the simulations; all other parameters were ACT–R defaults. The modeling results support the hypothesis that fluctuating activation and similarity-based interference are the key factors shaping working memory in sentence processing. We contrast the theory and empirical predictions with several related accounts of sentence-processing complexity.

This article addresses the question of how the processor decides on its initial strategy for syntactic ambiguity resolution. At a point of ambiguity, more than one analysis is possible. An effective strategy might be to adopt the analysis that has most frequently turned out to be correct in the past. Assuming that the world stays the same in most respects, the analysis that has most frequently been correct in the past should provide a good estimate of which analysis is most likely to be correct again. Hence, by adopting this analysis, the processor should make fewer errors than if it chose any other analysis

Readers are sensitive to the fact that verbs may allow multiple subcategorization frames that differ in their probability of occurrence. Although a verbÕs overall subcategorization preferences can be described probabilistically, underlying non-random factors may determine those probabilities. One potential factor is verb semantics: Many verbs show sense differences, and a verbÕs subcategorization profile can vary by sense. Thus, although find can occur with a direct object (DO) or a sentential complement (SC), when it is used to mean ÔlocateÕ it occurs only with a DO, whereas in its ÔrealizeÕ sense it is SC-biased, but can take either frame. We used corpus analyses to identify verbs that occur with both frames, and found that their subcategorization probabilities differ by sense. Off-line sentence completions demonstrated that contexts can promote a specific sense of a verb, which subsequently influenced subcategorization probability. Finally, in a self-paced reading time experiment, verbs occurred in target sentences containing either a structurally unambiguous or ambiguous SC, following a context favoring the verbÕs DO- or SC-biased sense. Sensebiasing context influenced reading times at that, and interacted with ambiguity in the disambiguating region. Thus, readers use sense-contingent subcategorization preferences during on-line language comprehension.

Copyright by Roger Levy 2005 ii

A central question for psycholinguistics concerns the role of grammatical constraints in online sentence processing. Many current theories maintain that the language processing mechanism constructs a parse or parses that are grammatically consistent with the whole of the perceived input each time it processes a word. Several bottom-up, dynamical models make a contrasting prediction: partial parses which are syntactically compatible with only a proper subpart of the input are sometimes constructed, at least temporarily. Three self-paced reading experiments probed for interference from such locally coherent structures. The first tested for a distracting effect of irrelevant Subject–Predicate interpretations of Noun Phrase–Verb Phrase sequences (e.g., The coach smiled at the player tossed a frisbee) on reading times. The second addressed the question of whether the interference effects can be treated as lexical interference, instead of involving the formation of locally coherent syntactic structures. The third replicated the reading time effects of the first two experiments with grammaticality judgments. We evaluate the dynamical account, comparing it to other approaches that also predict effects of local coherence, and arguing against accounts which rule out the formation of merely locally coherent structures.

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.