How to Combine Salience Factors for Arabic Pronoun Anaphora Resolution

Bouzid, Saoussen Mathlouthi; Trabelsi, Feriel Ben Fraj; Zribi, Chiraz Ben Othmane

doi:10.1109/aiccsa.2017.83

Cited by 2 publications

(1 citation statement)

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“…As a result, such pleonastic must be identified in any AR systems. For example, Aone and Bennett (1996), Kennedy and Boguraev (1996), and Bouzid, Trabelsi, & Zribi (2017) manually removed pleonastic pronouns, while other researchers employed machine learning techniques on the available corpus (Yifan, Musilek, Reformat, & Wyard-Scott, 2009). Anaphora resolution for Malay text can be seen in the works by Fazal Mohamed (2006), Yap (2011), andXian, Saloot, Ghazali, Bouzekri, Mahmud, andLukose (2016).…”

Section: Introductionmentioning

confidence: 99%

Classification of Short Possessive Clitic Pronoun Nya in Malay Text to Support Anaphor Candidate Determination

Noah

Aziz

2020

Journal of Information and Communication Technology

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Anaphor candidate determination is an important process in anaphora resolution (AR) systems. There are several types of anaphor, one of which is pronominal anaphor. Pronominal anaphor is an anaphor that involves pronouns. In some of the cases, certain pronouns can be used without referring to any situation or entity in a text, and this phenomenon is known as pleonastic. In the case of the Malay language, it usually occurs for the pronoun nya. The pleonastic that exists in every text causes a severe problem to the anaphora resolution systems. The process to determine the pleonastic nya is not the same as identifying the pleonastic ‘it’ in the English language, where the syntactic pattern could not be used because the structure of nya comes at the end of a word. As an alternative, semantic classes are used to identify the pleonastic itself and the anaphoric nya. In this paper, the automatic semantic tag was used to determine the type of nya, which at the same time could determine nya as an anaphor candidate. The new algorithms and MalayAR architecture were proposed. The results of the F-measure showed the detection of clitic nya as a separate word achieved a perfect 100% result. In comparison, the clitic nya as a pleonastic achieved 88%, clitic nya referring to humans achieved 94%, and clitic nya referring to non-humans achieved 63%. The results showed that the proposed algorithms were acceptable to solve the issue of the clitic nya as pleonastic, human referral as well as non-human referral.

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