Conceptual structures: information processing in mind and machine
Conceptual structures: information processing in mind and machine
Full text indexing based on lexical relations an application: software libraries
SIGIR '89 Proceedings of the 12th annual international ACM SIGIR conference on Research and development in information retrieval
Lexical knowledge representation and natural language processing
Artificial Intelligence - Special volume on natural language processing
Co-occurrences of antonymous adjectives and their contexts
Computational Linguistics
Structural ambiguity and lexical relations
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
Word association norms, mutual information, and lexicography
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
Estimating upper and lower bounds on the performance of word-sense disambiguation programs
ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
Word-sense disambiguation using statistical models of Roget's categories trained on large corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Is 1 noun worth 2 adjectives?: measuring relative feature utility
Information Processing and Management: an International Journal
Text-mining approach to evaluate terms for ontology development
Journal of Biomedical Informatics
Grading knowledge: extracting degree information from texts
Grading knowledge: extracting degree information from texts
Collocation extraction in Turkish texts using statistical methods
IceTAL'10 Proceedings of the 7th international conference on Advances in natural language processing
SemEval-2010 task 18: disambiguating sentiment ambiguous adjectives
Language Resources and Evaluation
A new fuzzy rule-based classification system for word sense disambiguation
Intelligent Data Analysis
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Recent corpus-based work on word sense disambiguation explores the application of statistical pattern recognition procedures to lexical co-occurrence data from very large text databases. In this paper we argue for a linguistically principled approach to disambiguation, in which relevant contextual clues are narrowly defined, in syntactic and semantic terms, and in which only highly reliable clues are exploited. Statistical methods play a definite role in this work, helping to organize and analyze data, but the disambiguation method itself does not employ statistical data or decision criteria. This approach results in improved understanding of the disambiguation problem both in general and on a word-specific basis and leads to broadly applicable and nearly errorless clues to word sense. The approach is illustrated by an experiment discriminating among the senses of adjectives, which have been relatively neglected in work on sense disambiguation. In particular, the paper assesses the potential of nouns for discriminating among the senses of adjectives that modify them. This assessment is based on an empirical study of five of the most frequent ambiguous adjectives in English: hard, light, old, right, and short. About three-quarters of all instances of these adjectives can be disambiguated almost errorlessly by the nouns they modify or by the syntactic constructions in which they occur. Such disambiguation requires only simple rules, which can be automated easily. Furthermore, a small number of semantic attributes supply a compact means of representing the noun clues in a very few rules. Clues other than nouns are required when modified nouns are not useable. The sense of an ambiguous modified noun may be needed to determine the relevant semantic attribute for disambiguation of a target adjective; and other adjectives, verbs, and grammatical constructions all show evidence of high reliability, and sometimes of high applicability, when they stand in specific, well-defined syntactic relations to the ambiguous adjective. Some of these clues, however, may be hard to automate.