Ted Pedersen: Automatic Resolution of Semantic Ambiguity in
Natural Language
Most words in natural language have multiple possible meanings. This
simple fact causes no end of difficulties for computer systems that
seek to understand and generate natural language. Existing methods
that resolve ambiguity in word meaning have proven difficult to
deploy on a wide scale because they are dependent on the availability
of specialized sources of knowledge that do not exist across a range of
domains. The long-term goal of this research is to develop techniques
that will liberate word sense disambiguation from these knowledge
acquisition bottlenecks and thereby simplify their integration into
natural language processing systems.
I will describe recent progress in meeting three specific objectives
that move us closer to this goal: 1) the development of methods to
discover the most relevant contextual features for determining the sense
of an ambiguous word; 2) the development of disambiguation algorithms
that learn from "just a few" manually created examples; and 3) the
development of unsupervised methods that allow any set of word meanings
to serve as the target of the disambiguation process.
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Last modified: Mon Apr 22 10:32:46 EDT 2002