Term Weighting Based on Document Revision History

• Tipo: Artigo em Revista Científica Internacional
• Data: 2011

Autores

Sérgio Nunes (Autor) FEUP; Cristina Ribeiro (Autor) FEUP; Gabriel David (Autor) FEUP

Revista

Título: Journal of the American Society for Information Science and Technology

Vol. 62 Nº 12

Páginas: 2471-2478

ISSN: 1532-2882

Editora: John Wiley & Sons

Indexação

ISI Web of Knowledge - 5 Citações

ISI Web of Science

Scopus - 7 Citações

INSPEC

COMPENDEX

Classificação Científica

FOS: Ciências da engenharia e tecnologias > Engenharia electrotécnica, electrónica e informática

Outras Informações

ID Authenticus: P-002-JAF

DOI: 10.1002/asi.21597

Abstract (EN): In real-world information retrieval systems, the underlying document collection is rarely stable or definitive. This work is focused on the study of signals extracted from the content of documents at different points in time for the purpose of weighting individual terms in a document. The basic idea behind our proposals is that terms that have existed for a longer time in a document should have a greater weight. We propose 4 term weighting functions that use each document's history to estimate a current term score. To evaluate this thesis, we conduct 3 independent experiments using a collection of documents sampled from Wikipedia. In the first experiment, we use data from Wikipedia to judge each set of terms. In a second experiment, we use an external collection of tags from a popular social bookmarking service as a gold standard. In the third experiment, we crowdsource user judgments to collect feedback on term preference. Across all experiments results consistently support our thesis. We show that temporally aware measures, specifically the proposed revision term frequency and revision term frequency span, outperform a term-weighting measure based on raw term frequency alone.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 8