Title:
Syntax-Semantics Interaction in Sentence Understanding
Syntax-Semantics Interaction in Sentence Understanding
Author(s)
Mahesh, Kavi
Advisor(s)
Editor(s)
Collections
Supplementary to
Permanent Link
Abstract
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, lexical and structural, ambiguities, but can also recover
from its errors in both syntactic and semantic ambiguity
resolution. The unified process can also explain the temporal
dependencies in syntax-semantics interactions. It shows how certain
decisions are made early and others delayed until further information
becomes available.
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. In addition we have developed a uniform representation
of syntactic and semantic interpretations using what are called
intermediate roles. These intermediate roles not only aid the dynamic
integration of knowledge types by the unified arbitrator, they also
provide a declarative record of the intermediate decisions made in
syntax-semantics interactions to enable the processor to recover from
its errors through repair rather than complete reprocessing. We
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.
Sponsor
Date Issued
1995
Extent
1297180 bytes
Resource Type
Text
Resource Subtype
Technical Report