Semantic role induction in Persian: An unsupervised approach by using probabilistic models.
Semantic roles describe the relation between a predicate (typically a verb) and its arguments. Semantic role labeling is a Natural Language Processing task that extracts these relations in the sentences. Different applications such as machine translation and question answering benefit from this leve...
| Publicado en: | Digital Scholarship in the Humanities Vol. 31; no. 1; pp. 181 - 204 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Oxford University Press / USA
4/1/2016
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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=114160254&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 114160254 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 2055768X JEO9 jtl: Digital Scholarship in the Humanities issn: 2055768X maglogo: N pubinfo: dt: 4/1/2016 vid: 31 iid: 1 pid: 622 pub: Oxford University Press / USA artinfo: ui: 114160254 10.1093/llc/fqu044 ppf: 181 ppct: 23 formats: fmt: @attributes: type: P size: 1.3MB tig: atl: Semantic role induction in Persian: An unsupervised approach by using probabilistic models. aug: au: Saeedi, Parisa Faili, Heshaam Shakery, Azadeh affil: School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran School of Computer Science, Institute for Research in Fundamental Sciences (IPM), P.O. Box 19395-5746, Tehran, Iran su: Semantics research Language research Lexicology Computational linguistics Digital humanities sug: subj: Semantics research Language research Lexicology Computational linguistics Digital humanities ab: Semantic roles describe the relation between a predicate (typically a verb) and its arguments. Semantic role labeling is a Natural Language Processing task that extracts these relations in the sentences. Different applications such as machine translation and question answering benefit from this level of semantic analysis. The creation of semantic role-annotated data is an obstacle to develop supervised learning systems, so we present a novel unsupervised approach to semantic role induction task. In our approach, which is formulized as a clustering method, the argument instances of the verb are clustered into semantic role classes specified for that verb. We present a Bayesian model for learning argument structure from un-annotated text and estimate the model parameters using expectation maximization method. Clustering of argument instances of a verb, which have semantic and syntactic similarities, can be a promising approach for unsupervised learning of their semantic roles. The only linguistic knowledge, which is prepared for linking the argument instances to semantic clusters is extracted from a verb valance lexicon. Our evaluation results on Persian language show that our system in both small and large training datasets works better than a strong baseline proposed by (Lang and Lapata 2010) which its idea is developed in Persian. We have used purity and inverse purity measures to assess the quality of the proposed semantic role clustering method. The results indicate the improvement about 9.73 and 1.65% in small dataset and 2.85 and 0.67% in large dataset in purity and inverse purity, respectively. 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: 2016 holdings: @attributes: islocal: N |
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