A multi-centrality index for graph-based keyword extraction

Autor(es)

Vega-Olivero, Didier A. ^{[1, 2]} ; Gomes, Pedro Spoljaric ^[3] ; Milios, Evangelos E. ^[4] ; Berton, Lilian ^[3]

Afiliação do(s) autor(es)

^[1] Indiana Univ, Sch Informat Comp & Engn, Bloomington, IN - USA

^[2] Univ Sao Paulo, Dept Comp & Math, Ribeirao Preto, SP - Brazil

^[3] Univ Fed Sao Paulo, Inst Sci & Technol, Sao Jose Dos Campos, SP - Brazil

^[4] Dalhousie Univ, Fac Comp Sci, Halifax, NS - Canada

• Tipo de documento: Artigo Científico
• Fonte: INFORMATION PROCESSING & MANAGEMENT; v. 56, n. 6 NOV 2019.
• Citações Web of Science: 0

Resumo

Keyword extraction aims to capture the main topics of a document and is an important step in natural language processing (NLP) applications. The use of different graph centrality measures has been proposed to extract automatic keywords. However, there is no consensus yet on how these measures compare in this task. Here, we present the multi-centrality index (MCI) approach, which aims to find the optimal combination of word rankings according to the selection of centrality measures. We analyze nine centrality measures (Betweenness, Clustering Coefficient, Closeness, Degree, Eccentricity, Eigenvector, K-Core, PageRank, Structural Holes) for identifying keywords in co-occurrence word-graphs representation of documents. We perform experiments on three datasets of documents and demonstrate that all individual centrality methods achieve similar statistical results, while the proposed MCI approach significantly outperforms the individual centralities, three clustering algorithms, and previously reported results in the literature. (AU)

• Processo FAPESP: 18/01722-3 - Aprendizado semissupervisionado via redes complexas: construção de redes, seleção e propagação de rótulos e aplicações
• Beneficiário: Lilian Berton
• Linha de fomento: Auxílio à Pesquisa - Regular
• Processo FAPESP: 18/24260-5 - Análise de dados espaço-temporais baseado em redes complexas
• Beneficiário: Didier Augusto Vega Oliveros
• Linha de fomento: Bolsas no Exterior - Estágio de Pesquisa - Pós-Doutorado
• Processo FAPESP: 16/23698-1 - Processos dinâmicos em aprendizado de máquina baseados em redes complexas
• Beneficiário: Didier Augusto Vega Oliveros
• Linha de fomento: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 15/50122-0 - Fenômenos dinâmicos em redes complexas: fundamentos e aplicações
• Beneficiário: Elbert Einstein Nehrer Macau
• Linha de fomento: Auxílio à Pesquisa - Temático