Cross Language Information Retrieval (CLIR): A Survey of Approaches for Exploring Web Across Languages
Suhas D. Pachpande1, Parag U. Bhalchandra2

1Suhas D. Pachpande*, Department of Computer Science, Sant Gadge Baba Amravati University, Amravati (MS), India.
2Parag U. Bhalchandra, School of Computational Sciences, Swami Ramanand Teerth Marathwada University, Nanded (MS), India.

Manuscript received on September 20, 2020. | Revised Manuscript received on November 02, 2020. | Manuscript published on November 10, 2021. | PP: 326-332 | Volume-10 Issue-1, November 2020 | Retrieval Number: 100.1/ijitee.K78330991120| DOI: 10.35940/ijitee.K7833.1110120
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Abstract: In the era of globalization, internet being accessible and affordable has gained huge popularity and is widely being used almost everywhere by Government, private organizations, companies, banks, etc. as well as by individuals. It has empowered its users to contribute to the creation of information on web enabling them to use their native languages which consequently has drastically increased the volume of web-accessible documents available in languages other than English. This exponential growth of information on the internet has also induced several challenges before the information retrieval systems. Most of the present monolingual information retrieval systems can retrieve documents in the language of query only, missing the information in other languages that may be more relevant to the user. The need of information retrieval systems to become multilingual has given rise to the research in Cross Language Information Retrieval (CLIR) which can cross the language barriers and retrieve more relevant results from documents in different languages. This article is a review of motivation, issues, work and challenges related to various CLIR approaches. Starting with the most fundamental approaches of translation, it is attempted to study and present a review of more advanced approaches for enhancing the retrieval results in CLIR proposed by various researchers working in this domain. 
Keywords: Cross Language Information Retrieval, Dictionary Based Translation, Corpus-Based Translation, Machine Translation, Lexical ambiguity, Bilingual dictionary, Term matching, Term frequency, Document ranking.
Scope of the Article: Frequency Selective Surface