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

this paper we focus on a particular domain of probabilistic linguistic knowledge in lexical production: the role of local probabilistic relations between words. 1

This paper investigates the role of resource allocation as a source of processing difficulty in human sentence comprehension. The paper proposes a simple informationtheoretic characterization of processing difficulty as the work incurred by resource reallocation during parallel, incremental, probabilistic disambiguation in sentence comprehension, and demonstrates its equivalence to the theory of Hale (2001), in which the difficulty of a word is proportional to its surprisal (its negative log-probability) in the context within which it appears. This proposal subsumes and clarifies findings that high-constraint contexts can facilitate lexical processing, and connects these findings to well-known models of parallel constraint-based comprehension. In addition, the theory leads to a number of specific predictions about the role of expectation in syntactic comprehension, including the reversal of locality-based difficulty patterns in syntactically constrained contexts, and conditions under which increased ambiguity facilitates processing. The paper examines a range of established results bearing on these predictions, and shows that they are largely consistent with the surprisal theory.

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We address the task of computing vector space representations for the meaning of word occurrences, which can vary widely according to context. This task is a crucial step towards a robust, vector-based compositional account of sentence meaning. We argue that existing models for this task do not take syntactic structure sufficiently into account. We present a novel structured vector space model that addresses these issues by incorporating the selectional preferences for words’ argument positions. This makes it possible to integrate syntax into the computation of word meaning in context. In addition, the model performs at and above the state of the art for modeling the contextual adequacy of paraphrases. 1

Function words, especially frequently occurring ones such as (the, that, and, and of), vary widely in pronunciation. Understanding this variation is essential both for cognitive modeling of lexical production and for computer speech recognition and synthesis. This study investigates which factors affect the forms of function words, especially whether they have a fuller pronunciation (e.g., , , 22 , ) or a more reduced or lenited pronunciation (e.g., ). It is based on over 8000 occurrences of the ten most frequent English function words in a four-hour sample from conversations from the Switchboard corpus. Ordinary linear and logistic regression models were used to examine variation in the length of the words, in the form of their vowel (basic, full, or reduced), and whether final obstruents were present or not. For all these measures, after controlling for segmental context, rate of speech, and other important factors, there are strong independent effects that made high-frequency monosyllabic function words more likely to be longer or have a fuller form (1) when neighboring disfluencies (such as filled pauses uh and um) indicate that the speaker was encountering problems in planning the utterance; (2) when the word is unexpected, i.e less predictable in context; (3) when the word is either utterance-initial or utterance-final. Looking at the phenomenon in a different way, frequent function words are more likely to be shorter and to have less full forms in fluent speech, in predictable positions or multi-word collocations, and utterance-internally. Also considered are other factors such as sex (women are more likely to use fuller forms, even after controlling for rate of speech, for example), and some of the dif...

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.

xi 1 Introduction 1 1.1 Intuitions and Evidence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Previous Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.2.1 The Classical Dual Route Model . . . . . . . . . . . . . . . . . . . . . . . 6 1.2.2 Seidenberg and McClelland 1989 . . . . . . . . . . . . . . . . . . . . . . 10 1.2.3 Plaut and Shallice 1993 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.2.4 Plaut et al. 1996: Naming . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.2.5 Bullinaria 1996 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 1.2.6 Plaut 1997: Lexical Decision . . . . . . . . . . . . . . . . . . . . . . . . . 15 1.2.7 Harm and Seidenberg 1998: Naming . . . . . . . . . . . . . . . . . . . . 16 1.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2 A New Computational Model 18 2.1 Principles . . . . . . . . . . . . . . . . . . . . . . . . ...

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