Extracting semantic hierarchies from a large on-line dictionary
ACL '85 Proceedings of the 23rd annual meeting on Association for Computational Linguistics
Is there content in empty heads?
COLING '90 Proceedings of the 13th conference on Computational linguistics - Volume 3
Extraction of semantic information from an ordinary English dictionary and its evaluation
COLING '88 Proceedings of the 12th conference on Computational linguistics - Volume 2
Can We Make Information Extraction More Adaptive?
Information Extraction: Towards Scalable, Adaptable Systems
Learning Rules for Large-Vocabulary Word Sense Disambiguation: A Comparison of Various Classifiers
NLP '00 Proceedings of the Second International Conference on Natural Language Processing
The interaction of knowledge sources in word sense disambiguation
Computational Linguistics
Verb sense disambiguation based on dual distributional similarity
Natural Language Engineering
Building accurate semantic taxonomies from monolingual MRDs
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Word sense disambiguation using optimised combinations of knowledge sources
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Word sense ambiguation: clustering related senses
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
Logic form transformation of WordNet and its applicability to question answering
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Building a large ontology for machine translation
HLT '93 Proceedings of the workshop on Human Language Technology
A hybrid relational approach for WSD: first results
COLING ACL '06 Proceedings of the 21st International Conference on computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
Implementing a sense tagger in a general architecture for text engineering
NeMLaP3/CoNLL '98 Proceedings of the Joint Conferences on New Methods in Language Processing and Computational Natural Language Learning
Learning rules for large vocabulary word sense disambiguation
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Combining weak knowledge sources for sense disambiguation
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Assessing the contribution of shallow and deep knowledge sources for word sense disambiguation
Language Resources and Evaluation
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The automatic construction of an IS_A taxonomy of noun senses from a machine readable dictionary (MRD) has long been sought, but achieved with only limited success. The task requires the solution to two problems: 1) To define an algorithm to automatically identify the genus or hypernym of a noun definition, and 2) to define an algorithm for lexical disambiguation of the genus term. In the last few years, effective methods for solving the first problem have been developed, but the problem of creating an algorithm for lexical disambiguation of the genus terms is one that has proven to be very difficult. In COLING 90 we described our initial work on the automatic creation of a taxonomy of noun senses from Longman's Dictionary of Contemporary English (LDOCE). The algorithm for lexical disambiguation of the genus term was accurate about 80% of the time and made use of the semantic categories, the subject area markings and the frequency of use information in LDOCE. In this paper we report a series of experiments which weight the three factors in various ways, and describe our improvements to the algorithm (to about 90% accuracy).