Word classification based on combined measures of distributional and semantic similarity

Authors:
Viktor Pekar;Steffen Staab
Affiliations:
Bashkir State University, Ufa, Russia;University of Karlsruhe
Venue:
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 2
Year:
2003

Citing 3
Cited 6

Towards text knowledge engineering

AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Extending a Lexical Ontology by a Combination of Distributional Semantics Signatures

EKAW '02 Proceedings of the 13th International Conference on Knowledge Engineering and Knowledge Management. Ontologies and the Semantic Web
Noun-phrase co-occurrence statistics for semiautomatic semantic lexicon construction

COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2

ADSS: an approach to determining semantic similarity

Advances in Engineering Software
Automatising the learning of lexical patterns: An application to the enrichment of WordNet by extracting semantic relationships from Wikipedia

Data & Knowledge Engineering
Learning semantic relatedness from term discrimination information

Expert Systems with Applications: An International Journal
Short Communication: ADSS: An approach to determining semantic similarity

Advances in Engineering Software
Combining contextual and structural information for supersense tagging of chinese unknown words

CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part I
Automatic extraction of semantic relationships for wordnet by means of pattern learning from wikipedia

NLDB'05 Proceedings of the 10th international conference on Natural Language Processing and Information Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

The paper addresses the problem of automatic enrichment of a thesaurus by classifying new words into its classes. The proposed classification method makes use of both the distributional data about a new word and the strength of the semantic relatedness of its target class to other likely candidate classes.