A New Text Semi-supervised Multi-label Learning Model Based on Using the Label-Feature Relations
| Autor: | Van-Quang Nguyen, Thi-Ngan Pham, Minh-Chau Nguyen, Thanh-Huyen Pham, Tri-Thanh Nguyen, Quang-Thuy Ha |
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| Rok vydání: | 2018 |
| Předmět: |
Exploit
Computer science business.industry Vietnamese Multi label learning 02 engineering and technology computer.software_genre language.human_language ComputingMethodologies_PATTERNRECOGNITION Feature (computer vision) 020204 information systems 0202 electrical engineering electronic engineering information engineering language 020201 artificial intelligence & image processing Artificial intelligence business computer Natural language processing |
| Zdroj: | Computational Collective Intelligence ISBN: 9783319984421 ICCCI (1) |
| DOI: | 10.1007/978-3-319-98443-8_37 |
| Popis: | Multi-label learning has become popular and omnipresent in many real-world problems, especially in text classification applications, in which an instance could belong to different classes simultaneously. Due to these label constraints, there are some challenges occurring in building multi-label data. Semi-supervised learning is one possible approach to exploit abundantly unlabeled data for enhancing the classification performance with a small labeled dataset. In this paper, we propose a solution to select the most influential label based on using the relations among the labels and features to a semi-supervised multi-label classification algorithm on texts. Experiments on two datasets of Vietnamese reviews and English emails of Enron show the positive effects of the proposal. |
| Databáze: | OpenAIRE |
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