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ECIR
2004
Springer

Complex Linguistic Features for Text Classification: A Comprehensive Study

10 years 29 days ago
Complex Linguistic Features for Text Classification: A Comprehensive Study
Abstract. Previous researches on advanced representations for document retrieval have shown that statistical state-of-the-art models are not improved by a variety of different linguistic representations. Phrases, word senses and syntactic relations derived by Natural Language Processing (NLP) techniques were observed ineffective to increase retrieval accuracy. For Text Categorization (TC) are available fewer and less definitive studies on the use of advanced document representations as it is a relatively new research area (compared to document retrieval). In this paper, advanced document representations have been investigated. Extensive experimentation on representative classifiers, Rocchio and SVM, as well as a careful analysis of the literature have been carried out to study how some NLP techniques used for indexing impact TC. Cross validation over 4 different corpora in two languages allowed us to gather an overwhelming evidence that complex nominals, proper nouns and word senses ar...
Alessandro Moschitti, Roberto Basili
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2004
Where ECIR
Authors Alessandro Moschitti, Roberto Basili
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