Disambiguating prepositional phrase attachments by using on-line dictionary definitions
Computational Linguistics - Special issue of the lexicon
Lexical and World Knowledge: Theoretical and Applied Viewpoints
Proceedings of the First SIGLEX Workshop on Lexical Semantics and Knowledge Representation
Computational Linguistics - Special issue on using large corpora: I
A class-based approach to word alignment
Computational Linguistics
Topical clustering of MRD senses based on information retrieval techniques
Computational Linguistics - Special issue on word sense disambiguation
The acquisition of lexical knowledge from combined machine-readable dictionary sources
ANLC '92 Proceedings of the third conference on Applied natural language processing
A concept-based adaptive approach to word sense disambiguation
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Extracting semantic hierarchies from a large on-line dictionary
ACL '85 Proceedings of the 23rd annual meeting on Association for Computational Linguistics
A taxonomy for English nouns and verbs
ACL '81 Proceedings of the 19th annual meeting on Association for Computational Linguistics
Parsing vs. text processing in the analysis of dictionary definitions
ACL '88 Proceedings of the 26th annual meeting on Association for Computational Linguistics
Word sense ambiguation: clustering related senses
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
Algorithm for automatic interpretation of noun sequences
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
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Machine-readable dictionaries have been regarded as a rich knowledge source from which various relations in lexical semantics can be effectively extracted. These semantic relations have been found useful for supporting a wide range of natural language processing tasks, from information retrieval to interpretation of noun sequences, and to resolution of prepositional phrase attachment. In this paper, we address issues related to problems in building a semantic hierarchy from machine-readable dictionaries: genus disambiguation, discovery of covert categories, and bilingual taxonomy. In addressing these issues, we will discuss the limiting factors in dictionary definitions and ways of eradicating these problems. We will also compare the taxonomy extracted in this way from a typical MRD and that of the WordNet. We argue that although the MRD-derived taxonomy is considerably flatter than the WordNet, it nevertheless provides a functional core for a variety of semantic relations and inferences which is vital in natural language processing.