Building thematic lexical resources by term categorization

Alberto Lavelli, Bernardo Magnini, Fabrizio Sebastiani

Research output: Contribution to journalArticle

Abstract

We discuss the automatic generation of thematic lexicons by means of term categorization, a novel task employing techniques from information retrieval (IR) and machine learning (ML). Specifically, we view the generation of such lexicons as an iterative process of learning previously unknown associations between terms and themes (i.e. disciplines, or fields of activity). The process is iterative, in that it generates, for each ci in a set = {ci,...,cm} of themes, a sequence L0i ⊆ L1i ⊆ ... ⊆ Lni of lexicons, bootstrapping from an initial lexicon L0i and a set of text corpora Θ = {θ0,...,θn-1} given as input. The method is inspired by text categorization, the discipline concerned with labelling natural language texts with labels from a predefined set of themes, or categories. However, while text categorization deals with documents represented as vectors in a space of terms, term categorization deals (dually) with terms represented as vectors in a space of documents, and labels terms (instead of documents) with themes. As a learning device we adopt boosting, since (a) it has demonstrated state-of-the-art effectiveness in a variety of text categorization applications, and (b) it naturally allows for a form of "data cleaning", thereby making the process of generating a thematic lexicon an iteration of generate-and-test steps.

Original languageEnglish
Pages (from-to)415-416
Number of pages2
JournalSIGIR Forum (ACM Special Interest Group on Information Retrieval)
Publication statusPublished - 2002
Externally publishedYes

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ASJC Scopus subject areas

  • Management Information Systems
  • Hardware and Architecture

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