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Ontology-based Tamil–English cross-lingual information retrieval system

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Abstract

Cross-lingual information retrieval (CLIR) systems facilitate users to query for information in one language and retrieve relevant documents in another language. In general, CLIR systems translate query in source language to target language and retrieve documents in target language based on the keywords present in the translated query. However, the presence of ambiguity in source and translated queries reduces the performance of the system. Ontology can be used to address this problem. The current approaches to ontology-based CLIR systems use manually constructed multilingual ontology, which is expensive. However, many methods exist to automatically construct ontology for any domain in English but not in other languages like Tamil. We propose a methodology for Tamil–English CLIR system by translating the Tamil query to English and retrieve pages in English to address these issues. Our approach uses a word sense disambiguation module to resolve the ambiguity in Tamil query. An automatically constructed ontology in English is used to address the ambiguity of English query. We have developed a morphological analyser for Tamil language, Tamil–English bilingual dictionary and named entity database to translate a Tamil query to English. The translated query is reformulated using ontology and the reformulated queries are given to a search engine to retrieve English documents from the Internet. We have evaluated our methodology for agriculture domain and the evaluation results show that our approach outperforms other approaches in terms of precision.

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Notes

  1. http://www.fallingrain.com/world/IN/25/.

  2. https://en.wikipedia.org/wiki/List_of_rivers_of_Tamil_Nadu.

  3. https://en.wikipedia.org/wiki/List_of_lakes_in_Tamil_Nadu.

  4. https://en.wikipedia.org/wiki/List_of_Tamil_people.

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Acknowledgements

We would like to thank the management of SSN Institutions for funding the High Performance Computing (HPC) lab, where this research is being carried out. We also thank the anonymous reviewers for their constructive comments, which helped us to improve the manuscript.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, SSN College of Engineering, Kalavakkam, 603 110, India

    D Thenmozhi & Chandrabose Aravindan

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  1. D Thenmozhi
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  2. Chandrabose Aravindan
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Correspondence to D Thenmozhi.

Appendix I

Appendix I

Tables 8, 9, 10, 11, 12, 13, 14, 15 and 16 show the significance of domain-specific ontology in the retrieval performance by comparing with other search methods.

Table 8 Performance comparison for the user query “Mutkalappai”.
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Table 9 Performance comparison for the user query “Uzhudhal Upakaranangkal”.
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Table 10 Performance comparison for the user query “Payinkaal”.
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Table 11 Performance comparison for the user query “Puussikal”.
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Table 12 Performance comparison for the user query “Kalappai”.
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Table 13 Performance comparison for the user query “VeNkaaram”.
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Table 14 Performance comparison for the user query “EthiruutikaL”.
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Table 15 Performance comparison for the user query “Thunai Marunthu PoruL”.
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Table 16 Performance comparison for the user query “ManjaLin payankaL”.
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Thenmozhi, D., Aravindan, C. Ontology-based Tamil–English cross-lingual information retrieval system. Sādhanā 43, 157 (2018). https://doi.org/10.1007/s12046-018-0942-7

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  • DOI: https://doi.org/10.1007/s12046-018-0942-7

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