Volume 12, Issue 2 (9-2015)
JSDP 2015, 12(2): 13-22 |
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khodadadi H, rahati quchani S, estaji A. Contrast Relation Recognition in Persian discourse using supervised learning methods. JSDP 2015; 12 (2) :13-22
URL: http://jsdp.rcisp.ac.ir/article-1-45-en.html
URL: http://jsdp.rcisp.ac.ir/article-1-45-en.html
islamic azad university
Abstract: (7060 Views)
Discourse is a part of language that intend is used to communicate. A discourse relation recognition system can identify one or more relation between the textual units in a discourse. Like other languages, Contrast relation is a one of the available relations in Persian discourse. Contrast relation recognition in discourse is useful for generation and perception of discourse, paraphrasing and summarization systems and et al. This relation in one discourse is often detected by discourse marker such as “اما” and “ولی”, But in some situation these markers are removed and relation recognition is difficult. For this reason we have proposed to use of some feature for relation recognition. These features are: tense of verbs, word pairs, and et al. In this paper, a Corpus of Research Center of Intelligent Signal Processing has been used to collect 5000 instances of contrast and 5000 other relations then created feature vector for each instance. We used three supervised methods for classification: SVM, KNN ,Parzen Window and combine of this classifiers. Finally, the best result achieved by combine classifier that accuracy is 87.13.
Keywords: Contrast Relation Recognition, Discourse, Discourse Marker, Natural Language Processing, Supervised Learning
Type of Study: Research |
Subject:
Paper
Received: 2013/05/22 | Accepted: 2015/02/13 | Published: 2015/09/30 | ePublished: 2015/09/30
Received: 2013/05/22 | Accepted: 2015/02/13 | Published: 2015/09/30 | ePublished: 2015/09/30
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