Co-occurrence graph-based context adaptation: a new unsupervised approach to word sense disambiguation.
Word sense disambiguation (WSD) is the task of selecting correct sense for an ambiguous word in its context. Since WSD is one of the most challenging tasks in various text processing systems, improving its accuracy can be very beneficial. In this article, we propose a new unsupervised method based o...
| Publicado en: | Digital Scholarship in the Humanities Vol. 36; no. 2; pp. 449 - 472 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Oxford University Press / USA
Jun2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=152743601&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 152743601 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 2055768X JEO9 jtl: Digital Scholarship in the Humanities issn: 2055768X maglogo: N pubinfo: dt: Jun2021 vid: 36 iid: 2 pid: 622 pub: Oxford University Press / USA artinfo: ui: 152743601 10.1093/llc/fqz048 ppf: 449 ppct: 23 formats: fmt: @attributes: type: P size: 1.3MB tig: atl: Co-occurrence graph-based context adaptation: a new unsupervised approach to word sense disambiguation. aug: au: Rahmani, Saeed Fakhrahmad, Seyed Mostafa Sadreddini, Mohammad Hadi affil: Computer Science and Engineering Department, Shiraz University , Shiraz, Iran su: Ambiguity Vocabulary Physiological adaptation sug: subj: Ambiguity Vocabulary Physiological adaptation ab: Word sense disambiguation (WSD) is the task of selecting correct sense for an ambiguous word in its context. Since WSD is one of the most challenging tasks in various text processing systems, improving its accuracy can be very beneficial. In this article, we propose a new unsupervised method based on co-occurrence graph created by monolingual corpus without any dependency on the structure and properties of the language itself. In the proposed method, the context of an ambiguous word is represented as a sub-graph extracted from a large word co-occurrence graph built based on a corpus. Most of the words are connected in this graph. To clarify the exact sense of an ambiguous word, its senses and relations are added to the context graph, and various similarity functions are employed based on the senses and context graph. In the disambiguation process, we select senses with highest similarity to the context graph. As opposite to other WSD methods, the proposed method does not use any language-dependent resources (e.g. WordNet) and it just uses a monolingual corpus. Therefore, the proposed method can be employed for other languages. Moreover, by increasing the size of corpus, it is possible to enhance the accuracy of WSD. Experimental results on English and Persian datasets show that the proposed method is competitive with existing supervised and unsupervised WSD approaches. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: © 2019 EADH: The European Association for Digital Humanities. item: Digital Scholarship in the Humanities holder: Oxford University Press / USA dt: @attributes: year: 2021 holdings: @attributes: islocal: N |
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