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| Natura: | Artículo científico |
| Lingua: | en |
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International Journal of Combinatorial Optimization Problems and Informatics
2015
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| Accesso online: | https://www.redalyc.org/articulo.oa?id=265239212002 |
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| _version_ | 1866812725152186368 |
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| author | Víctor Carrera-Trejo |
| author_facet | Víctor Carrera-Trejo |
| contents | Latent Dirichlet Allocation complement in the vector space model for Multi-Label Text Classification Víctor Carrera-Trejo Grigori Sidorov Sabino Miranda-Jiménez Marco Moreno Ibarra Rodrigo Cadena Martínez Computación tf idf Multi 21578 Reuters In text classification task one of the main problems is to choose which features give the best results. Various features can be used like words, n-grams, syntactic n-grams of various types (POS tags, dependency relations, mixed, etc.), or a combinations of these features can be considered. Also, algorithms for dimensionality reduction of these sets of features can be applied, like Latent Dirichlet Allocation (LDA). In this paper, we consider multi-label text classification task and apply various feature sets. We consider a subset of multi-labeled files from the Reuters-21578 corpus. We use traditional tf-IDF values of the features and tried both considering and ignoring stop words. We also tried several combinations of features, like bigrams and unigrams. We also experimented with adding LDA results into Vector Space Models as new features. These last experiments obtained the best results. 2015 artículo científico 2007-1558 https://www.redalyc.org/articulo.oa?id=265239212002 en http://www.redalyc.org/revista.oa?id=2652 International Journal of Combinatorial Optimization Problems and Informatics application/pdf International Journal of Combinatorial Optimization Problems and Informatics International Journal of Combinatorial Optimization Problems and Informatics (México) Num.1 Vol.6 |
| format | Artículo científico |
| id | redalyc_265239212002 |
| language | en |
| publishDate | 2015 |
| publisher | International Journal of Combinatorial Optimization Problems and Informatics |
| spellingShingle | Latent Dirichlet Allocation complement in the vector space model for Multi-Label Text Classification Víctor Carrera-Trejo Computación tf idf Multi 21578 Reuters Latent Dirichlet Allocation complement in the vector space model for Multi-Label Text Classification Víctor Carrera-Trejo Grigori Sidorov Sabino Miranda-Jiménez Marco Moreno Ibarra Rodrigo Cadena Martínez Computación tf idf Multi 21578 Reuters In text classification task one of the main problems is to choose which features give the best results. Various features can be used like words, n-grams, syntactic n-grams of various types (POS tags, dependency relations, mixed, etc.), or a combinations of these features can be considered. Also, algorithms for dimensionality reduction of these sets of features can be applied, like Latent Dirichlet Allocation (LDA). In this paper, we consider multi-label text classification task and apply various feature sets. We consider a subset of multi-labeled files from the Reuters-21578 corpus. We use traditional tf-IDF values of the features and tried both considering and ignoring stop words. We also tried several combinations of features, like bigrams and unigrams. We also experimented with adding LDA results into Vector Space Models as new features. These last experiments obtained the best results. 2015 artículo científico 2007-1558 https://www.redalyc.org/articulo.oa?id=265239212002 en http://www.redalyc.org/revista.oa?id=2652 International Journal of Combinatorial Optimization Problems and Informatics application/pdf International Journal of Combinatorial Optimization Problems and Informatics International Journal of Combinatorial Optimization Problems and Informatics (México) Num.1 Vol.6 |
| title | Latent Dirichlet Allocation complement in the vector space model for Multi-Label Text Classification |
| topic | Computación tf idf Multi 21578 Reuters |
| url | https://www.redalyc.org/articulo.oa?id=265239212002 |