Abstract
Natural Language Processing (NLP) includes Tasks such as Information Extraction (IE), text summarization, and question and answering, all of which require identifying all the information about an entity exists in the discourse. Therefore a system capable of studying Co-reference Resolution (CR) will contribute to the successful completion of these Tasks. In this paper we are going to study process of Co-reference Resolution and represent a system capable of identifying Co-reference mentions for first the time in Farsi corpora. So we should consider three main steps of Farsi Corpus with Co-reference annotation, system of Mention Recognition and its domain, and the algorithm of predicting Co-reference Mentions as the basis of our study. Therefore, in first step, we prepare a Corpus with suitable labels, and this Corpus as first Farsi corpus having Mention and Co-reference labels can be the basis of many researches related to mention Detection (MD) and CR. Also using such corpus and studying rules and priorities among the mentions, we present a system that identifies the mentions and negative and positive examples. Then by using learning algorithm such as SVM, Neural Network and Decision Tree on extracted samples we have evaluated models for predicting Co-reference mentions in Farsi Language. Finally, we conclude that the performance of neural network is better than other learners.
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Nazaridoust, M., Bidgoli, B.M., Nazaridoust, S. (2014). Co-reference Resolution in Farsi Corpora. In: Jamshidi, M., Kreinovich, V., Kacprzyk, J. (eds) Advance Trends in Soft Computing. Studies in Fuzziness and Soft Computing, vol 312. Springer, Cham. https://doi.org/10.1007/978-3-319-03674-8_15
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DOI: https://doi.org/10.1007/978-3-319-03674-8_15
Publisher Name: Springer, Cham
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