Implicit prosody and contextual bias in silent reading

Eye-movement research on implicit prosody has found effects of lexical stress on syntactic ambiguity resolution, suggesting that metrical well-formedness constraints interact with syntactic category assignment. Building on these findings, the present eyetracking study investigates whether contextual bias can modulate the effects of metrical structure on syntactic ambiguity resolution in silent reading. Contextual bias and potential stress-clash in the ambiguous region were crossed in a 2 2 design. Participants read biased context sentences followed by temporarily ambiguous test sentences. In the three-word ambiguous region, main effects of lexical stress were dominant, while early effects of context were absent. Potential stress clash yielded a significant increase in first-pass regressions and re-reading probability across the three words. In the disambiguating region, the disambiguating word itself showed increased processing difficulty (lower skipping and increased re-reading probability) when the disambiguation engendered a stress clash configuration, while the word immediately following showed main effects of context in those same measures. Taken together, effects of lexical stress upon eye movements were swift and pervasive across first-pass and second-pass measures, while effects of context were relatively delayed. These results indicate a strong role for implicit meter in guiding parsing, one that appears insensitive to higher-level constraints. Our findings are problematic for two classes of models, the two-stage garden-path model and the constraint-based competition-integration model, but can be explained by a variation on the two-stage model, the unrestricted race model

Learning to Disambiguate Syntactic Relations

Natural Language is highly ambiguous, on every level. This article describes a fast broad-coverage state-of-the-art parser that uses a carefully hand-written grammar and probability-based machine learning approaches on the syntactic level. It is shown in detail which statistical learning models based on Maximum-Likelihood Estimation (MLE) can support a highly developed linguistic grammar in the disambiguation process

Learning to Disambiguate Syntactic Relations

Many extensions to text-based, data-intensive knowledge management approaches, such as Information Retrieval or Data Mining, focus on integrating the impressive recent advances in language technology. For this, they need fast, robust parsers that deliver linguistic data which is meaningful for the subsequent processing stages. This paper introduces such a parsing system and discusses some of its disambiguation techniques which are based on learning from a large syntactically annotated corpus. The paper is organized as follows. Section 2 explains the motivations for writing the parser, and why it profits from Dependency grammar assumptions. Section 3 gives a brief introduction to the parsing system and to evaluation questions. Section 4 presents the probabilistic models and the conducted experiments in detail

Sense Tagging: Semantic Tagging with a Lexicon

Sense tagging, the automatic assignment of the appropriate sense from some lexicon to each of the words in a text, is a specialised instance of the general problem of semantic tagging by category or type. We discuss which recent word sense disambiguation algorithms are appropriate for sense tagging. It is our belief that sense tagging can be carried out effectively by combining several simple, independent, methods and we include the design of such a tagger. A prototype of this system has been implemented, correctly tagging 86% of polysemous word tokens in a small test set, providing evidence that our hypothesis is correct.Comment: 6 pages, uses aclap LaTeX style file. Also in Proceedings of the SIGLEX Workshop "Tagging Text with Lexical Semantics

Syntactic structure assembly in human parsing: A computational model based on competitive inhibition and a lexicalist grammar

We present the design, implementation and simulation results of a psycholinguistic model of human syntactic processing that meets major empirical criteria. The parser operates in conjunction with a lexicalist grammar and is driven by syntactic information associated with heads of phrases. The dynamics of the model are based on competition by lateral inhibition ('competitive inhibition'). Input words activate lexical frames (i.e. elementary trees anchored to input words) in the mental lexicon, and a network of candidate 'unification links' is set up between frame nodes. These links represent tentative attachments that are graded rather than all-or-none. Candidate links that, due to grammatical or 'treehood' constraints, are incompatible, compete for inclusion in the final syntactic tree by sending each other inhibitory signals that reduce the competitor's attachment strength. The outcome of these local and simultaneous competitions is controlled by dynamic parameters, in particular by the Entry Activation and the Activation Decay rate of syntactic nodes, and by the Strength and Strength Build-up rate of Unification links. In case of a successful parse, a single syntactic tree is returned that covers the whole input string and consists of lexical frames connected by winning Unification links. Simulations are reported of a significant range of psycholinguistic parsing phenomena in both normal and aphasic speakers of English: (i) various effects of linguistic complexity (single versus double, center versus right-hand self-embeddings of relative clauses; the difference between relative clauses with subject and object extraction; the contrast between a complement clause embedded within a relative clause versus a relative clause embedded within a complement clause); (ii) effects of local and global ambiguity, and of word-class and syntactic ambiguity (including recency and length effects); (iii) certain difficulty-of-reanalysis effects (contrasts between local ambiguities that are easy to resolve versus ones that lead to serious garden-path effects); (iv) effects of agrammatism on parsing performance, in particular the performance of various groups of aphasic patients on several sentence types