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Title:
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Syntax-Semantics Interaction in Sentence Understanding
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Author:
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Kavi Mahesh
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Email:
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click here to access email
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Homepage:
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http://crl.nmsu.edu/users/mahesh/
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Degree Awarded:
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Georgia Institute of Technology
, Department of Computer Science
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Degree Date:
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1995
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Linguistic Subfield(s):
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Computational Linguistics
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Director(s):
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Kurt Eiselt
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Abstract:
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Natural language is the primary mode of human communication. Developing a complete and well-specified computational model of language understanding is a difficult problem. Understanding a natural language sentence requires the application of many types of knowledge, such as syntactic, semantic, and conceptual knowledge, to resolve the many types of ambiguities that abound in natural language. Most unresolved issues in both psychological and computational modeling of sentence understanding are concerned with the questions of when should each of the various types of knowledge be applied in processing a sentence and how should the different types of knowledge be integrated to select unique interpretations of sentences.
In this work, we have developed a model of sentence understanding called COMPERE (Cognitive Model of Parsing and Error Recovery). Our model was built on the hypothesis that a sentence processor has an architecture with separate representations of the different types of knowledge but a single unified process that integrates the different types of knowledge. We have shown that such an architecture addresses the modularity debate by demonstrating how the same sentence processor can produce seemingly modular behaviors in some situations and interactive behaviors in other situations. We have also shown how the unified arbitrating process can not only resolve both syntactic and semantic ambiguities, but also recover from its errors in both syntactic and semantic ambiguity resolution.
We have developed a parsing algorithm called Head-Signaled Left-Corner parsing to identify the time course of points in the sentence where decisions are to be made. This algorithm decides when to make a commitment and when to delay a syntactic attachment. We have also developed a simple arbitration algorithm for combining information coming from multiple knowledge sources and for resolving any conflicts between them. We also present a theoretical framework for formal analyses of the performance of sentence processors in various situations. These analyses indicate that the HSLC parsing algorithm, along with incremental interactions between syntax and semantics controlled by the unified arbitrator, reduces the amount of local ambiguity and working memory requirements in processing a sentence. We also present certain psychological predictions made by the COMPERE model.
We conclude from this study that our model of sentence understanding, with its unified process applied independently to multiple knowledge sources, provides an answer to the modularity debate and explains, better than other possible architectures, how and why the human sentence processor produces the wide variety of behaviors that it does.
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