Using corpus statistics and WordNet relations for sense identification
Computational LinguisticsPublished 1 March 1998
Claudia Leacock, George A. Miller, Martin Chodorow
Citations441
SJR quartileQ1
SJR score1.15
SNIP4.16
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
A statistical classifier is described that combines topical context with local cues to identify a word sense and is used to disambiguate a noun, a verb, and an adjective.
Abstract
Corpus-based approaches to word sense identification have flexibility and generality but suffer from a knowledge acquisition bottleneck. We show how knowledge-based techniques can be used to open the bottleneck by automatically locating training corpora. We describe a statistical classifier that combines topical context with local cues to identify a word sense. The classifier is used to disambiguate a noun, a verb, and an adjective. A knowledge base in the form of WordNet's lexical relations is used to automatically locate training examples in a general text corpus. Test results are compared with those from manually tagged training examples.
Keywords
Computer Science
International Journal of LexicographyIntroduction to WordNet: An On-line Lexical Database<sup>*</sup>
4,207 Citations1990George A. Miller, Richard Beckwith +3 more
Standard alphabetical procedures for organizing lexical information put together words that are spelled alike and scatter words with similar or related meanings haphazardly through the list.
BiometrikaTHE POPULATION FREQUENCIES OF SPECIES AND THE ESTIMATION OF POPULATION PARAMETERS
3,227 Citations1953I. J. Good
Unsupervised word sense disambiguation rivaling supervised methods
2,408 Citations1995David Yarowsky
An unsupervised learning algorithm for sense disambiguation that, when trained on unannotated English text, rivals the performance of supervised techniques that require time-consuming hand annotations.
Word-sense disambiguation using statistical models of Roget's categories trained on large corpora
670 Citations1992David Yarowsky
A program that disambiguates English word senses in unrestricted text using statistical models of the major Roget's Thesaurus categories, enabling training on unrestricted monolingual text without human intervention.
Computers and the HumanitiesA method for disambiguating word senses in a large corpus
534 Citations1992William A. Gale, Kenneth Church +1 more
The proposed method was designed to disambiguate senses that are usually associated with different topics using a Bayesian argument that has been applied successfully in related tasks such as author identification and information retrieval.
One sense per discourse
524 Citations1992William A. Gale, Kenneth Church +1 more
An experiment confirmed the hypothesis that if a polysemous word such as sentence appears two or more times in a well-written discourse, it is extremely likely that they will all share the same sense and found that the tendency to share sense in the same discourse is extremely strong.
Cambridge University Press eBooksSemantic Interpretation and the Resolution of Ambiguity
474 Citations1987Graeme Hirst
One sense per collocation
384 Citations1993David Yarowsky
This paper shows that for certain definitions of collocation, a polysemous word exhibits essentially only one sense per collocation and utilizes this property in a disambiguation algorithm that achieves precision of 92% using combined models of very local context.
Decision lists for lexical ambiguity resolution
362 Citations1994David Yarowsky
This paper presents a statistical decision procedure for lexical ambiguity resolution that exploits both local syntactic patterns and more distant collocational evidence, generating an efficient, effective, and highly perspicuous recipe for resolving a given ambiguity.
Word sense disambiguation using a second language monolingual corpus
268 Citations1994Ido Dagan, Alon Itai
Word-sense disambiguation using decomposable models
206 Citations1994Rebecca Bruce, Janyce Wiebe
This paper describes a method for formulating probabilistic models that use multiple contextual features for word-sense disambiguation, without requiring untested assumptions regarding the form of the model.
Word sense disambiguation with very large neural networks extracted from machine readable dictionaries
160 Citations1990Jean Véronis, Nancy Ide
This paper describes a means for automatically building very large neural networks (VLNNs) from definition texts in machine-readable dictionaries, and demonstrates the use of these networks for word sense disambiguation.
Corpus-based statistical sense resolution
155 Citations1993Claudia Leacock, Geoffrey G. Towell +1 more
Three corpus-based statistical sense resolution methods studied here attempt to infer the correct sense of a polysemous word by using knowledge about patterns of word cooccurrences, based on Bayesian decision theory, neural, networks, and content vectors.
WordNet and Distributional Analysis: A Class-based Approach to Lexical Discovery
74 Citations1992Philip Resnik
An estimate of mutual information is used to calculate what nouns a verb can take as its subjects and objects, based on distributions found within a large corpus of naturally occurring text.
arXiv (Cornell University)A Bayesian hybrid method for context-sensitive spelling correction
66 Citations1996Andrew R. Golding
This paper takes Yarowsky's work as a starting point, applying decision lists to the problem of context-sensitive spelling correction, and finds that further improvements can be obtained by taking into account not just the single strongest piece of evidence, but ALL the available evidence.
Information Storage and RetrievalLearning to disambiguate
60 Citations1973Stephen F. Weiss
Automatic resolution of ambiguities from natural language text is investigated and, by use of the learning technique, disambiguation may be achieved with virtually no a priori decisions.
Towards Building Contextual Representations of Word Senses Using Statistical Models
29 Citations1993Claudia Leacock, Geoffrey G. Towell +1 more
Stanford University eBooksAmbiguity in language learning: computational and cognitive models
21 Citations1996Hinrich Schütze
It is shown here that--contrary to much skepticism in contemporary linguistic theories--analogy, induction, distribution and association are powerful sources of information and can, in concert with general cognitive innateknowledge, but without language-specific innate knowledge, learn important properties of language that are thought to be innate by many.
The COCOON platform (University of Paris)Robust learning, smoothing, and parameter tying on syntactic ambiguity resolution
14 Citations1995Tung‐Hui Chiang, Keh‐Yih Su +1 more
A discrimination-and robustness-oriented learning algorithm is proposed in this paper for minimizing the error rate and a parameter tying scheme is proposed to tie those highly correlated but unreliably estimated parameters together so that the parameters can be better trained in the learning process.