Word co-occurrence features for text classification

Figueiredo, Fabio Soares; Rocha, Leonardo; Couto, Thierson; Salles, Thiago; Gonçalves, Marcos André; Meira, Wagner

doi:10.1016/j.is.2011.02.002

Cited by 98 publications

(56 citation statements)

References 24 publications

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“…Figueiredo et al [7] proposed a data treatment strategy to generate new discriminative features, called compound-features for the sake of text classification. These c-features are composed by terms that co-occur in documents without any restrictions on order or distance between terms within a document.…”

Section: Data Treatment Strategymentioning

confidence: 99%

Automatic Discovery of Association Orders between Name and Aliases from the Web using Anchor Texts-based Co-occurrences

B¹,

Jayabhaduri²

2012

IJCA

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Many celebrities and experts from various fields may have been referred by not only their personal names but also by their aliases on web. Aliases are very important in information retrieval to retrieve complete information about a personal name from the web, as some of the web pages of the person may also be referred by his aliases. The aliases for a personal name are extracted by previously proposed alias extraction method. In information retrieval, the web search engine automatically expands the search query on a person name by tagging his aliases for complete information retrieval thereby improving recall in relation detection task and achieving a significant mean reciprocal rank (MRR) of search engine. For the further substantial improvement on recall and MRR from the previously proposed methods, our proposed method will order the aliases based on their associations with the name using the definition of anchor texts-based co-occurrences between name and aliases in order to help the search engine tag the aliases according to the order of associations. The association orders will automatically be discovered by creating an anchor texts-based co-occurrence graph between name and aliases. Ranking support vector machine (SVM) will be used to create connections between name and aliases in the graph by performing ranking on anchor texts-based co-occurrence measures. The hop distances between nodes in the graph will lead to have the associations between name and aliases. The hop distances will be found by mining the graph. The proposed method will outperform previously proposed methods, achieving substantial growth on recall and MRR.

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Section: Data Treatment Strategymentioning

confidence: 99%

Automatic Discovery of Association Orders between Name and Aliases from the Web using Anchor Texts-based Co-occurrences

B¹,

Jayabhaduri²

2012

IJCA

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“…Existem muitas evidências na literatura de que representações que incorporam alguma informação de relação entre termos melhora os resultados de tarefas como recuperação de informação (Wong et al, 1985;Billhardt et al, 2002;Pôssas et al, 2002), classificação de textos (Lewis, 1992;Keikha et al, 2009;Figueiredo et al, 2011), agrupamento de textos (Beil et al, 2002;Zhang et al, 2010;Rossi and Rezende, 2011;Marcacini et al, 2012a;Kalogeratos and Likas, 2012;Cheng et al, 2013b) ou extração de tópicos (Wallach, 2006;Wu et al, 2010;Zhu et al, 2012;Lau et al, 2013). Diversos modelos para representação dos documentos foram propostos na literatura visando capturar a informação de relação entre termos, destacando-se os modelos baseados em frases ou termos compostos (Pôssas et al, 2002;Figueiredo et al, 2011;Rossi and Rezende, 2011), o Modelo de Espaço de Vetores Generalizado 6 (MEVG) e suas extensões (Wong et al, 1985;Billhardt et al, 2002;Farahat and Kamel, 2011;Kalogeratos and Likas, 2012;Cheng et al, 2013b), modelos de tópicos não-probabilísticos, como o Latent Semantic Analysis (LSA) (Deerwester et al, 1990) ou o Non-negative Matrix Factorization (NMF) (Lee and Seung, 1999) e modelos de tópicos probabilísticos e suas extensões (Hofmann, 1999;Blei et al, 2003;Wallach, 2006;Kim et al, 2012;Zhu et al, 2012;Lau et al, 2013;Gao et al, 2013).…”

Section: Introductionunclassified

“…Diversos modelos para representação dos documentos foram propostos na literatura visando capturar a informação de relação entre termos, destacando-se os modelos baseados em frases ou termos compostos (Pôssas et al, 2002;Figueiredo et al, 2011;Rossi and Rezende, 2011), o Modelo de Espaço de Vetores Generalizado 6 (MEVG) e suas extensões (Wong et al, 1985;Billhardt et al, 2002;Farahat and Kamel, 2011;Kalogeratos and Likas, 2012;Cheng et al, 2013b), modelos de tópicos não-probabilísticos, como o Latent Semantic Analysis (LSA) (Deerwester et al, 1990) ou o Non-negative Matrix Factorization (NMF) (Lee and Seung, 1999) e modelos de tópicos probabilísticos e suas extensões (Hofmann, 1999;Blei et al, 2003;Wallach, 2006;Kim et al, 2012;Zhu et al, 2012;Lau et al, 2013;Gao et al, 2013). Os modelos de tópicos são as abordagens mais interessantes uma vez que elas apresentam bons resultados 4 Do inglês Vector Space Model -VSM.…”

Section: Introductionunclassified

“…Uma representação construída com os novos atributos extraídosé uma oportunidade de incorporar conhecimento de domínio aos dados (Guyon and Elisseeff, 2003). Entretanto, a extração eficiente de termos correlacionados para construção da representação de documentos aindaé um grande desafio de pesquisa (Figueiredo et al, 2011;Cheng et al, 2013b;Gao et al, 2013). Os modelos para representação de documentos que exploram a correlação entre termos normalmente enfrentam um grande desafio para manter um bom equilíbrio entre (i) a quantidade de dimensões obtidas, (ii) o esforço computacional e (iii) a interpretabilidade das novas dimensões obtidas.…”

Section: Introductionunclassified

“…Os desafios (i) e (ii) estão fortemente relacionados, uma vez que a quantidade de possíveis combinações entre os termos de uma coleção contendo T termosé 2 T . Assim, existe um grande esforço computacional para a extração dessas combinações em grandes coleções de documentos e para determinar quais combinações são significativas para a aplicação (Figueiredo et al, 2011). Mesmo em modelos como o Wallach's Bigram Topic Model (Wallach, 2006) que possui um tratamento probabilístico teórico bem desenvolvido das correlações de termos, existe uma sobrecarga no esforço computacional uma vez queé preciso estimar T 2 K parâmetros para obter K tópicos e T termos.…”

Section: Introductionunclassified

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Extração de tópicos baseado em agrupamento de regras de associação

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A quantitative analysis of the temporal effects on automatic text classification

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Asso for Info Science & Tech

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Automatic text classification (TC) continues to be a relevant research topic and several TC algorithms have been proposed. However, the majority of TC algorithms assume that the underlying data distribution does not change over time. In this work, we are concerned with the challenges imposed by the temporal dynamics observed in textual data sets. We provide evidence of the existence of temporal effects in three textual data sets, reflected by variations observed over time in the class distribution, in the pairwise class similarities, and in the relationships between terms and classes. We then quantify, using a series of full factorial design experiments, the impact of these effects on four well‐known TC algorithms. We show that these temporal effects affect each analyzed data set differently and that they restrict the performance of each considered TC algorithm to different extents. The reported quantitative analyses, which are the original contributions of this article, provide valuable new insights to better understand the behavior of TC algorithms when faced with nonstatic (temporal) data distributions and highlight important requirements for the proposal of more accurate classification models.

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Word co-occurrence features for text classification

Cited by 98 publications

References 24 publications

Automatic Discovery of Association Orders between Name and Aliases from the Web using Anchor Texts-based Co-occurrences

Automatic Discovery of Association Orders between Name and Aliases from the Web using Anchor Texts-based Co-occurrences

Extração de tópicos baseado em agrupamento de regras de associação

A quantitative analysis of the temporal effects on automatic text classification

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