Kernel methods for word sense disambiguation
Many applications of natural language processing (NLP) need an accurate resolution of various ambiguities existing in natural language. The task of fulfilling this need is also called word sense disambiguation (WSD). WSD is to resolve the correct sense for an instance of a polysemous word. On the ot...
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| Veröffentlicht in: | The Artificial intelligence review 2016-06, Vol.46 (1), p.41-58 |
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| creator | Li, Xiangjun Qing, Song Zhang, Huawei Wang, Tinghua Yang, Huping |
| description | Many applications of natural language processing (NLP) need an accurate resolution of various ambiguities existing in natural language. The task of fulfilling this need is also called word sense disambiguation (WSD). WSD is to resolve the correct sense for an instance of a polysemous word. On the other hand, as one of the most popular machine learning approaches, kernel methods have attracted significant interest in recent years and have exhibited fairly high performance in a wide variety of learning tasks. In this paper, we present a survey of the research progress of kernel-based WSD techniques. We start by introducing some preliminary knowledge concerning WSD and kernel methods. Then, a review of the main approaches in the literature is presented, focusing on the following issues: context representation, kernel design and learning algorithms. We also provide some further discussions on the kernel-based WSD approaches. Finally, open problems and future directions are discussed. |
| doi_str_mv | 10.1007/s10462-015-9455-5 |
| format | Article |
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The task of fulfilling this need is also called word sense disambiguation (WSD). WSD is to resolve the correct sense for an instance of a polysemous word. On the other hand, as one of the most popular machine learning approaches, kernel methods have attracted significant interest in recent years and have exhibited fairly high performance in a wide variety of learning tasks. In this paper, we present a survey of the research progress of kernel-based WSD techniques. We start by introducing some preliminary knowledge concerning WSD and kernel methods. Then, a review of the main approaches in the literature is presented, focusing on the following issues: context representation, kernel design and learning algorithms. We also provide some further discussions on the kernel-based WSD approaches. 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| subjects | Algorithms Analysis Artificial Intelligence Computational linguistics Computer engineering Computer Science Data mining Dictionaries Expert systems Information retrieval Kernels Knowledge Language processing Learning Machine learning Methods Natural language interfaces Natural language processing Principal components analysis Representations Semantics Support vector machines Tasks Word sense disambiguation |
| title | Kernel methods for word sense disambiguation |
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