Paper Info
Title
Automatic selection of class labels from a thesaurus for an effective semantic tagging of corpora
Abstract
It is widely accepted that tagging text with semantic information would improve the quality of lexical learning in corpus-based NLP methods. However available on-line taxonomies are rather entangled and introduce an unnecessary level of ambiguity. The noise produced by the redundant number of tags often overrides the advantage of semantic tagging. In this paper we propose an automatic method to select from WordNet a subset of domain-appropriate categories that effectively reduce the overambiguity of WordNet, and help at identifying and categorise relevant language patterns in a more compact way. The method is evaluated against a manually tagged corpus, SEMCOR.
Year
DOI
Venue
1997
10.3115/974557.974612
ANLP
Keywords
Field
DocType
tagging text,effective semantic,categorise relevant language pattern,domain-appropriate category,automatic selection,lexical learning,semantic tagging,class label,available on-line taxonomy,automatic method,corpus-based nlp method,semantic information,redundant number
Information retrieval,Computer science,Semantic information,Pattern language,Artificial intelligence,Natural language processing,WordNet,Ambiguity
Conference
Citations 
PageRank 
References 
7
0.69
13
Authors
2
Name
Order
Citations
PageRank
Alessandro Cucchiarelli122636.38
paola velardi21553163.66