Machine Learning Algorithms for Portuguese Named Entity Recognition
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| Format: | Artículo científico |
| Language: | en |
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Asociación Española para la Inteligencia Artificial
2007
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| _version_ | 1876460660839153664 |
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| author | Ruy Luiz Milidiú |
| author_facet | Ruy Luiz Milidiú |
| contents | Machine Learning Algorithms for Portuguese Named Entity Recognition Ruy Luiz Milidiú Julio Cesar Duarte Roberto Cavalcante Ingeniería Named Entities Machine Learning Supervised Training Named Entity Recognition (NER) is an important task in Natural Language Processing. It provides keyfeatures that help on more elaborated document management and information extraction tasks. In thispaper, we propose seven machine learning approaches that use HMM, TBL and SVM to solve PortugueseNER. The performance of each modeling approach is empirically evaluated. The SVM-based extractor showsa 88:11% F-score, which is our best observed value, slightly better than TBL. This is very competitive whencompared to state-of-the-art extractors for similar Portuguese NER problems. Our HMM has reasonableprecision and accuracy and does not require any expert knowledge. This is an advantage for our HMM overthe other approaches. The experimental results suggest that Machine Learning can be useful in PortugueseNER. They also indicate that HMM, TBL and SVM perform well in this natural language processing task 2007 artículo científico 1137-3601 https://www.redalyc.org/articulo.oa?id=92503610 en http://www.redalyc.org/revista.oa?id=925 Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial application/pdf Asociación Española para la Inteligencia Artificial Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial (España) Num.36 Vol.11 |
| format | Artículo científico |
| id | redalyc_92503610 |
| institution | Redalyc |
| language | en |
| publishDate | 2007 |
| publisher | Asociación Española para la Inteligencia Artificial |
| spellingShingle | Machine Learning Algorithms for Portuguese Named Entity Recognition Ruy Luiz Milidiú Ingeniería Named Entities Machine Learning Supervised Training Machine Learning Algorithms for Portuguese Named Entity Recognition Ruy Luiz Milidiú Julio Cesar Duarte Roberto Cavalcante Ingeniería Named Entities Machine Learning Supervised Training Named Entity Recognition (NER) is an important task in Natural Language Processing. It provides keyfeatures that help on more elaborated document management and information extraction tasks. In thispaper, we propose seven machine learning approaches that use HMM, TBL and SVM to solve PortugueseNER. The performance of each modeling approach is empirically evaluated. The SVM-based extractor showsa 88:11% F-score, which is our best observed value, slightly better than TBL. This is very competitive whencompared to state-of-the-art extractors for similar Portuguese NER problems. Our HMM has reasonableprecision and accuracy and does not require any expert knowledge. This is an advantage for our HMM overthe other approaches. The experimental results suggest that Machine Learning can be useful in PortugueseNER. They also indicate that HMM, TBL and SVM perform well in this natural language processing task 2007 artículo científico 1137-3601 https://www.redalyc.org/articulo.oa?id=92503610 en http://www.redalyc.org/revista.oa?id=925 Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial application/pdf Asociación Española para la Inteligencia Artificial Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial (España) Num.36 Vol.11 |
| title | Machine Learning Algorithms for Portuguese Named Entity Recognition |
| topic | Ingeniería Named Entities Machine Learning Supervised Training |
| url | https://www.redalyc.org/articulo.oa?id=92503610 |