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The Analysis of Noun Sequences using
Semantic Information Extracted from OnLine Dictionaries, PhD thesis, Georgetown
University
October 1, 1996
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Download Document
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BibTex
Authors

Lucy Vanderwende
Publication Type
TechReport
Pages
312
Number
MSR-TR-95-57

Abstract

Related Info
Abstract
This dissertation describes a computational system for the automatic analysis of noun sequences
in unrestricted text. Noun sequences (also known as noun compounds or complex nominals)
have several characteristics which prove to be obstacles to their automatic interpretation. First,
the creation of noun sequences is highly productive in English; it is not possible to store all the
noun sequences that will be encountered while processing text. Second, their interpretation is not
recoverable from syntactic or morphological analysis. Interpreting a noun sequence, i.e., finding
the relation between the nouns in a noun sequence, requires semantic information, both in limited
domains and in unrestricted text. The semantic analysis in previous computational systems relied
heavily on the availability of domain-specific knowledge bases; these have always been
handcoded. In this dissertation, we will describe a new approach to the problem of interpreting
noun sequences; we also propose a new classification schema for noun sequences, consisting of
14 basic relations/classes. The approach involves a small set of general rules for interpreting NSs
which makes use of semantic information extracted from the definitions in on-line dictionaries;
the process for automatically acquiring semantic information will be described in detail. Each
general rule can be considered as the configuration of semantic features and attributes on the
nouns which provide evidence for a particular noun sequence interpretation; the rules access a set
of 28 semantic features and attributes. The rules test relatedness between the semantic
information and the nouns in the noun sequence. The score for each rule is not determined by the
presence or absence of semantic features and attributes, but by the degree to which the nouns are
related. The results show that this system interprets 53% of the noun sequences in previously
unseen text. An analysis of the results indicates that additional rules are needed and that the
semantic information found provides good results, but some semantic information is still
missing. For these tests, only information extracted from the definitions were used; on-line
dictionaries also contain example sentences which should be exploited, as well as the definitions
of words other those in the noun sequence.
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