1,594 research outputs found
Distinguishing Word Senses in Untagged Text
This paper describes an experimental comparison of three unsupervised
learning algorithms that distinguish the sense of an ambiguous word in untagged
text. The methods described in this paper, McQuitty's similarity analysis,
Ward's minimum-variance method, and the EM algorithm, assign each instance of
an ambiguous word to a known sense definition based solely on the values of
automatically identifiable features in text. These methods and feature sets are
found to be more successful in disambiguating nouns rather than adjectives or
verbs. Overall, the most accurate of these procedures is McQuitty's similarity
analysis in combination with a high dimensional feature set.Comment: 11 pages, latex, uses aclap.st
Training and Scaling Preference Functions for Disambiguation
We present an automatic method for weighting the contributions of preference
functions used in disambiguation. Initial scaling factors are derived as the
solution to a least-squares minimization problem, and improvements are then
made by hill-climbing. The method is applied to disambiguating sentences in the
ATIS (Air Travel Information System) corpus, and the performance of the
resulting scaling factors is compared with hand-tuned factors. We then focus on
one class of preference function, those based on semantic lexical collocations.
Experimental results are presented showing that such functions vary
considerably in selecting correct analyses. In particular we define a function
that performs significantly better than ones based on mutual information and
likelihood ratios of lexical associations.Comment: To appear in Computational Linguistics (probably volume 20, December
94). LaTeX, 21 page
The interaction of knowledge sources in word sense disambiguation
Word sense disambiguation (WSD) is a computational linguistics task likely to benefit from the tradition of combining different knowledge sources in artificial in telligence research. An important step in the exploration of this hypothesis is to determine which linguistic knowledge sources are most useful and whether their combination leads to improved results.
We present a sense tagger which uses several knowledge sources. Tested accuracy exceeds 94% on our evaluation corpus.Our system attempts to disambiguate all content words in running text rather than limiting itself to treating a restricted vocabulary of words. It is argued that this approach is more likely to assist the creation of practical systems
One Homonym per Translation
The study of homonymy is vital to resolving fundamental problems in lexical
semantics. In this paper, we propose four hypotheses that characterize the
unique behavior of homonyms in the context of translations, discourses,
collocations, and sense clusters. We present a new annotated homonym resource
that allows us to test our hypotheses on existing WSD resources. The results of
the experiments provide strong empirical evidence for the hypotheses. This
study represents a step towards a computational method for distinguishing
between homonymy and polysemy, and constructing a definitive inventory of
coarse-grained senses.Comment: 8 pages, including reference
Selective Sampling for Example-based Word Sense Disambiguation
This paper proposes an efficient example sampling method for example-based
word sense disambiguation systems. To construct a database of practical size, a
considerable overhead for manual sense disambiguation (overhead for
supervision) is required. In addition, the time complexity of searching a
large-sized database poses a considerable problem (overhead for search). To
counter these problems, our method selectively samples a smaller-sized
effective subset from a given example set for use in word sense disambiguation.
Our method is characterized by the reliance on the notion of training utility:
the degree to which each example is informative for future example sampling
when used for the training of the system. The system progressively collects
examples by selecting those with greatest utility. The paper reports the
effectiveness of our method through experiments on about one thousand
sentences. Compared to experiments with other example sampling methods, our
method reduced both the overhead for supervision and the overhead for search,
without the degeneration of the performance of the system.Comment: 25 pages, 14 Postscript figure
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