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Fang, Sheng; Fan, Xien; Thomas, Gary; Ng, Peter A.

doi:10.1023/a:1011262119636

Cited by 6 publications

(1 citation statement)

References 39 publications

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“…With knowledge-based techniques, the main assumption is that disambiguation systems need sources of knowledge to determine the proper meaning of a lexeme that has multiple senses (Otegi, Arregi, Ansa, & Agirre, 2015;Sheng, Fan, Thomas, & Ng, 2001). Hence, these approaches are similar to dictionary-based ones in that both rely on sources of knowledge for disambiguation purposes.…”

Section: Literature Reviewmentioning

confidence: 99%

Lexical Ambiguity in Arabic Information Retrieval: The Case of Six Web-Based Search Engines

Omar¹,

Aldawsari²

2020

IJEL

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In recent years, both research and industry have shown an increasing interest in developing reliable information retrieval (IR) systems that can effectively address the growing demands of users worldwide. In spite of the relative success of IR systems in addressing the needs of users and even adapting to their environments, many problems remain unresolved. One main problem is lexical ambiguity which has negative impacts on the performance and reliability of IR systems. To date, lexical ambiguity has been one of the most frequently reported problems in the Arabic IR systems despite the development of different word sense disambiguation (WSD) techniques. This is largely attributed to the limitations of such techniques in addressing the issue of linguistic peculiarities. Hence, this study addresses these limitations by exploring the reasons for lexical ambiguity in IR applications in Arabic as one step towards reliable and practical solutions. For this purpose, the performances of six search engines Google, Bing, Baidu, Yahoo, Yandex, and Ask are evaluated. Results indicate that lexical ambiguities in Arabic IR applications are mainly due to the unique morphological and orthographic system of the Arabic language, in addition to its diglossia and the multiple colloquial dialects where sometimes mutual intelligibility is not achieved. For better disambiguation and IR performances in Arabic, this study proposes that clustering models based on supervised machine learning theory should be trained to address the morphological diversity of Arabic and its unique orthographic system. Search engines should also be adapted to the geographic location of the users in order to address the issue of vernacular dialects of Arabic. They should also be trained to automatically identify the different dialects. Finally, search engines should consider all varieties of Arabic and be able to interpret the queries regardless of the particular language adopted by the user.

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Section: Literature Reviewmentioning

confidence: 99%