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Journal articles on the topic 'Cross-language information retrieval'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Lokhande, Kalyani, and Dhanashree Tayade. "English-Marathi Cross Language Information Retrieval System." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 8 (2017): 112. http://dx.doi.org/10.23956/ijarcsse.v7i8.34.

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Nowadays, diﬀerent types of content in diﬀerent languages are available on World Wide Web and their usage is increasing rapidly. Cross Language Information Retrieval (CLIR) deals with retrieval of documents in another language than the language of the requested query. Various researchers worked on Cross Language Information Retrieval systems for Indian languages using diﬀerent translation approaches. There is still CLIR system to be developed which allow user to retrieve Marathi documents when English query is given. In the proposed English to Marathi Cross Language Information Retrieval syste

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2

Gey, Fredric, Noriko Kando, and Carol Peters. "Cross language information retrieval." ACM SIGIR Forum 36, no. 2 (2002): 72–80. http://dx.doi.org/10.1145/792550.792564.

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3

Nie, Jian-Yun. "Cross-Language Information Retrieval." Synthesis Lectures on Human Language Technologies 3, no. 1 (2010): 1–125. http://dx.doi.org/10.2200/s00266ed1v01y201005hlt008.

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4

Elayeb, Bilel, and Ibrahim Bounhas. "Arabic Cross-Language Information Retrieval." ACM Transactions on Asian and Low-Resource Language Information Processing 15, no. 3 (2016): 1–44. http://dx.doi.org/10.1145/2789210.

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5

Oard, Douglas W. "Interactive cross-language information retrieval." ACM SIGIR Forum 35, no. 1 (2001): 1–3. http://dx.doi.org/10.1145/948716.948718.

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6

Giang, Lam Tung, Vo Trung Hung, and Huynh Cong Phap. "Building Proximity Models for Cross Language Information Retrieval." Journal of Science and Technology: Issue on Information and Communications Technology 1 (August 31, 2015): 8. http://dx.doi.org/10.31130/jst.2015.5.

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In information retrieval systems, the proximity of query terms has been employed to enable ranking models to go beyond the ”bag of words” assumption and it can promote scores of documents where the matched query terms are close to each other. In this article, we study the integration of proximity models into cross-language information retrieval systems. The new proximity models are proposed and incorporated into existing cross-language information systems by combining the proximity score and the original score to re-rank retrieved documents. The experiment results show that the proposed models

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7

Jena, Gouranga Charan, and Siddharth Swarup Rautaray. "A comprehensive survey on cross-language information retrieval system." Indonesian Journal of Electrical Engineering and Computer Science 14, no. 1 (2019): 127. http://dx.doi.org/10.11591/ijeecs.v14.i1.pp127-134.

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Cross language information retrieval (CLIR) is a retrieval process in which the user fires queries in one language to retrieve information from another (different) language. The diversity of information and language barriers are the serious issues for communication and cultural exchange across the world. To solve such barriers, Cross language information retrieval system, are nowadays in strong demand. CLIR is a subset of Information Retrieval (IR) system. Information Retrieval deals with finding useful information from a large collection of unstructured, structured and semi-structured data to

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8

Hedlund, Turid. "Dictionary-based cross-language information retrieval." ACM SIGIR Forum 38, no. 1 (2004): 76. http://dx.doi.org/10.1145/986278.986297.

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9

., Pratibha Bajpai. "CROSS LANGUAGE INFORMATION RETRIEVAL: IN INDIAN LANGUAGE PERSPECTIVE." International Journal of Research in Engineering and Technology 03, no. 22 (2014): 46–52. http://dx.doi.org/10.15623/ijret.2014.0322010.

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10

Sheridan, Páraic, Martin Wechsler, and Peter Schäuble. "Cross-language speech retrieval." ACM SIGIR Forum 31, SI (1997): 99–108. http://dx.doi.org/10.1145/278459.258544.

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11

Diekema, Anne R. "Translation events in cross-language information retrieval." ACM SIGIR Forum 38, no. 1 (2004): 75. http://dx.doi.org/10.1145/986278.986296.

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12

K.Dwivedi, Sanjay, and Ganesh Chandra. "A Survey on Cross Language Information Retrieval." International Journal on Cybernetics & Informatics 5, no. 1 (2016): 127–42. http://dx.doi.org/10.5121/ijci.2016.5113.

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13

Zhou, Dong, Mark Truran, Tim Brailsford, Vincent Wade, and Helen Ashman. "Translation techniques in cross-language information retrieval." ACM Computing Surveys 45, no. 1 (2012): 1–44. http://dx.doi.org/10.1145/2379776.2379777.

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14

Gey, Fredric C., Noriko Kando, and Carol Peters. "Cross-Language Information Retrieval: the way ahead." Information Processing & Management 41, no. 3 (2005): 415–31. http://dx.doi.org/10.1016/j.ipm.2004.06.006.

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15

Orengo, Viviane Moreira, and Christian Huyck. "Relevance feedback and cross-language information retrieval." Information Processing & Management 42, no. 5 (2006): 1203–17. http://dx.doi.org/10.1016/j.ipm.2005.12.003.

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16

Wang, Jianqiang, and Douglas W. Oard. "Matching meaning for cross-language information retrieval." Information Processing & Management 48, no. 4 (2012): 631–53. http://dx.doi.org/10.1016/j.ipm.2011.09.003.

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17

KANG, IN-SU, SEUNG-HOON NA, and JONG-HYEOK LEE. "Translation Probabilities in Cross-Language Information Retrieval." International Journal of Computer Processing of Languages 18, no. 02 (2005): 115–36. http://dx.doi.org/10.1142/s0219427905001237.

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18

Jena, Gouranga Charan, and Siddharth Swarup Rautaray. "A comprehensive survey on cross-language information retrieval system." Indonesian Journal of Electrical Engineering and Computer Science 14, no. 1 (2019): 127–34. https://doi.org/10.11591/ijeecs.v14.i1.pp127-134.

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Cross language information retrieval (CLIR) is a retrieval process in which the user fires queries in one language to retrieve information from another (different) language. The amount of information generated in different languages and disseminated via the World Wide Web and social media platforms is growing exponentially. It is important to ensure that online information resources and services are efficiently and equitably accessible to all users, regardless of their linguistic and cultural backgrounds. The need for exploring and building more specialized information systems that enable spea

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19

Suhas, D. Pachpande, and U. Bhalchandra Parag. "Cross Language Information Retrieval (CLIR): A Survey of Approaches for Exploring Web Across Languages." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 10, no. 1 (2020): 326–32. https://doi.org/10.35940/ijitee.K7833.1110120.

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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 retrieva

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20

Prasath, Rajendra, and Sudeshna Sarkar. "Cross-Language Information Retrieval with Incorrect Query Translations." Polibits 54 (July 31, 2016): 33–42. http://dx.doi.org/10.17562/pb-54-5.

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21

Levow, Gina-Anne, Douglas W. Oard, and Philip Resnik. "Dictionary-based techniques for cross-language information retrieval." Information Processing & Management 41, no. 3 (2005): 523–47. http://dx.doi.org/10.1016/j.ipm.2004.06.012.

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22

Polajnar, Emil. "Using restricted CCA for cross-language information retrieval." Communications in Statistics - Simulation and Computation 46, no. 6 (2017): 4618–26. http://dx.doi.org/10.1080/03610918.2015.1122054.

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23

Polajnar, Emil. "Using Lasso RCCA for cross-language information retrieval." Communications in Statistics - Simulation and Computation 47, no. 9 (2017): 2739–48. http://dx.doi.org/10.1080/03610918.2017.1359287.

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24

Freitas-Junior, Hermes R., Berthier Ribeiro-Neto, Rodrigo F. Vale, Alberto H. F. Laender, and Luciano R. S. Lima. "Categorization-driven cross-language retrieval of medical information." Journal of the American Society for Information Science and Technology 57, no. 4 (2006): 501–10. http://dx.doi.org/10.1002/asi.20320.

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25

Inparaprapan, Chatchai, and Kraisak Kesorn. "A Modified Cosine Similarity for Cross Language Information Retrieval." Advanced Materials Research 931-932 (May 2014): 1348–52. http://dx.doi.org/10.4028/www.scientific.net/amr.931-932.1348.

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Since millions of documents are available on the Internet, some documents contain similar content but they are written in different languages by various authors. Unfortunately, the existing search engines do not support to all documents that are relevant to a single language query. Therefore, several researchers have put a huge effort to overcome such a problem. The major problems of a cross language search engine include 1) how to store information in a unify model and represent information of multiple languages documents effectively and 2) how to rank the retrieved multiple language document

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26

Chen, Jiangping. "Toward a unified retrieval outcome analysis framework for cross-language information retrieval." Proceedings of the American Society for Information Science and Technology 42, no. 1 (2006): n/a. http://dx.doi.org/10.1002/meet.14504201106.

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27

Talvensaari, Tuomas, Martti Juhola, Jorma Laurikkala, and Kalervo Järvelin. "Corpus-based cross-language information retrieval in retrieval of highly relevant documents." Journal of the American Society for Information Science and Technology 58, no. 3 (2007): 322–34. http://dx.doi.org/10.1002/asi.20495.

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28

Bajpai, Pratibha, Parul Verma, and Syed Q. Abbas. "English-Hindi Cross Language Information Retrieval System: Query Perspective." Journal of Computer Science 14, no. 5 (2018): 705–13. http://dx.doi.org/10.3844/jcssp.2018.705.713.

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29

Gao, Jianfeng, Jian-Yun Nie, and Ming Zhou. "Statistical query translation models for cross-language information retrieval." ACM Transactions on Asian Language Information Processing 5, no. 4 (2006): 323–59. http://dx.doi.org/10.1145/1236181.1236184.

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30

Kishida, Kazuaki. "Technical issues of cross-language information retrieval: a review." Information Processing & Management 41, no. 3 (2005): 433–55. http://dx.doi.org/10.1016/j.ipm.2004.06.007.

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31

Bellaachia, Abdelghani, and Ghita Amor-Tijani. "Proper nouns in English-Arabic cross language information retrieval." Journal of the American Society for Information Science and Technology 59, no. 12 (2008): 1925–32. http://dx.doi.org/10.1002/asi.20913.

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32

Feng, Tao. "A Comparative Optimization Model of Japanese Literature Characteristics for Cognitive Retrieval of Cross-Language Information." Computational Intelligence and Neuroscience 2022 (September 14, 2022): 1–10. http://dx.doi.org/10.1155/2022/8195075.

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Cross-language information cognitive retrieval has grown in importance as a study area due to the multilingual character of Internet resources and the diversifying languages spoken by users. This paper analyses and illustrates the key characteristics of Japanese literature from three aspects: ideological structure, structural form, and emotional expression, and makes a straightforward comparison with Chinese literature using a number of well-known Japanese literary masterpieces as examples. An important aspect of this literature is that it is full of lingering feelings, leaving room for the re

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33

Clough, Paul. "Image retrieval: Large-scale evaluation of cross-language image retrieval systems." Bulletin of the American Society for Information Science and Technology 33, no. 3 (2008): 18–21. http://dx.doi.org/10.1002/bult.2007.bult1720330311.

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34

Taan, Ayman A., Shafiq Ur Rehman Khan, Ali Raza, Ayaz Muhammad Hanif, and Hira Anwar. "Comparative Analysis of Information Retrieval Models on Quran Dataset in Cross-Language Information Retrieval Systems." IEEE Access 9 (2021): 169056–67. http://dx.doi.org/10.1109/access.2021.3126168.

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35

Linli, Peng. "A Cross-Language Information Retrieval Method Based on Multi-Task Learning." MALCOM: Indonesian Journal of Machine Learning and Computer Science 4, no. 3 (2024): 853–62. http://dx.doi.org/10.57152/malcom.v4i3.1384.

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This study introduces a novel Cross-Language Information Retrieval (CLIR) method employing multi-task learning and soft parameter sharing to enhance neural retrieval models' feature extraction across languages. The approach integrates an interaction-based neural retrieval model with a semantic-based text classification model, exchanging hidden vectors for richer feature representation. Experimental results across four language pairs—English-Chinese, English-Arabic, English-French, and English-German—demonstrate significant performance improvements. The proposed method achieved the highest Mean

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36

Kraaij, Wessel, Jian-Yun Nie, and Michel Simard. "Embedding Web-Based Statistical Translation Models in Cross-Language Information Retrieval." Computational Linguistics 29, no. 3 (2003): 381–419. http://dx.doi.org/10.1162/089120103322711587.

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Although more and more language pairs are covered by machine translation (MT) services, there are still many pairs that lack translation resources. Cross-language information retrieval (CLIR) is an application that needs translation functionality of a relatively low level of sophistication, since current models for information retrieval (IR) are still based on a bag of words. The Web provides a vast resource for the automatic construction of parallel corpora that can be used to train statistical translation models automatically. The resulting translation models can be embedded in several ways

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37

Yang, Xianben, and Wei Zhang. "Graph Convolutional Networks for Cross-Modal Information Retrieval." Wireless Communications and Mobile Computing 2022 (January 6, 2022): 1–8. http://dx.doi.org/10.1155/2022/6133142.

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In recent years, due to the wide application of deep learning and more modal research, the corresponding image retrieval system has gradually extended from traditional text retrieval to visual retrieval combined with images and has become the field of computer vision and natural language understanding and one of the important cross-research hotspots. This paper focuses on the research of graph convolutional networks for cross-modal information retrieval and has a general understanding of cross-modal information retrieval and the related theories of convolutional networks on the basis of litera

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38

Pirkola, Ari, Deniz Puolamäki, and Kalervo Järvelin. "Applying query structuring in cross-language retrieval." Information Processing & Management 39, no. 3 (2003): 391–402. http://dx.doi.org/10.1016/s0306-4573(02)00091-2.

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39

Archana.M and Sumithra Devi K.A Dr. "MULTILINGUAL INFORMATION RETRIEVAL BASED ON KNOWLEDGE CREATION TECHNIQUES." International Journal of Computer Science, Engineering and Information Technology (IJCSEIT) 1, no. 4 (2011): 27–35. https://doi.org/10.5281/zenodo.3532232.

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As the information access across languages increases, the importance of a system that supports querybased searching with the presence of multilingual also grows. Gathering the information in different natural language is the most difficult task, which requires huge resources like database and digital libraries. Cross language information retrieval (CLIR) enables to search in multilingual document collections using the native language which can be supported by the different data mining techniques. This paper deals with various data mining techniques that can be used for solving the problems enc

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40

Larkey, Leah S., and Margaret E. Connell. "Structured queries, language modeling, and relevance modeling in cross-language information retrieval." Information Processing & Management 41, no. 3 (2005): 457–73. http://dx.doi.org/10.1016/j.ipm.2004.06.008.

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41

Schulz, S., U. Hahn, and K. Markó. "MorphoSaurus." Methods of Information in Medicine 44, no. 04 (2005): 537–45. http://dx.doi.org/10.1055/s-0038-1634005.

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Summary Objectives: We propose an interlingua-based indexing approach to account for the particular challenges that arise in the design and implementation of cross-language document retrieval systems for the medical domain. Methods: Documents, as well as queries, are mapped to a language-independent conceptual layer on which retrieval operations are performed. We contrast this approach with the direct translation of German queries to English ones which, subsequently, are matched against English documents. Results: We evaluate both approaches, interlingua-based and direct translation, on a larg

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42

KARANIKOLAS, NIKITAS N. "Low cost, cross-language and cross-platform Information Retrieval and Documentation tools." Journal of Computing and Information Technology 15, no. 1 (2007): 71. http://dx.doi.org/10.2498/cit.1000759.

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43

Feng, Xiaoyan, and Yanfang Zhou. "English Audio Language Retrieval Based on Adaptive Speech-Adjusting Algorithm." Complexity 2021 (July 2, 2021): 1–12. http://dx.doi.org/10.1155/2021/2762180.

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For the purpose of language retrieval for English listening, this paper designs and implements a cross-language information retrieval system for English listening. Different implementation methods of cross-language information retrieval, query, and translation are analyzed. The system adopts cross-language information retrieval technology based on bilingual dictionaries. According to the cross-language retrieval system of the existing bilingual dictionaries and monolingual dictionaries, based on the design and implementation of the fuzzy search dictionary lookup mechanism, the existing diction

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44

CHEW, PETER A., BRETT W. BADER, STEPHEN HELMREICH, AHMED ABDELALI, and STEPHEN J. VERZI. "An information-theoretic, vector-space-model approach to cross-language information retrieval." Natural Language Engineering 17, no. 1 (2011): 37–70. http://dx.doi.org/10.1017/s1351324910000185.

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AbstractIn this article, we demonstrate several novel ways in which insights from information theory (IT) and computational linguistics (CL) can be woven into a vector-space-model (VSM) approach to information retrieval (IR). Our proposals focus, essentially, on three areas: pre-processing (morphological analysis), term weighting, and alternative geometrical models to the widely used term-by-document matrix. The latter include (1) PARAFAC2 decomposition of a term-by-document-by-language tensor, and (2) eigenvalue decomposition of a term-by-term matrix (inspired by Statistical Machine Translati

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45

Wang, Xiao Bo, Fan Zhao, Xiao Li, and Rong Hui Zhang. "Cross Language Query Expansion Approach for CIMS Based on Weighted D-S Evidence Theory." Key Engineering Materials 620 (August 2014): 534–43. http://dx.doi.org/10.4028/www.scientific.net/kem.620.534.

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With the Computer Integrated Manufacturing System and Information Technology rapid development, rapid retrieval multilingual becomes one of the hot spots in Machine Translation. The cross-language information retrieval (CLIR) provides a convenient way, enabling users to use their own familiar language to submit queries to retrieve documents in another language. Basic query expansion is one of the effective methods to improve recall of information retrieval. There are many researchers have proposed many extension methods, but most methods are simply added to the query expansion terms. If we do

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46

Jena, Gouranga Charan. "A Framework for English-Odia Cross-Language Information Retrieval System." International Journal of Advanced Trends in Computer Science and Engineering 9, no. 2 (2020): 2320–25. http://dx.doi.org/10.30534/ijatcse/2020/214922020.

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47

Volk, Martin, Bärbel Ripplinger, Špela Vintar, Paul Buitelaar, Diana Raileanu, and Bogdan Sacaleanu. "Semantic annotation for concept-based cross-language medical information retrieval." International Journal of Medical Informatics 67, no. 1-3 (2002): 97–112. http://dx.doi.org/10.1016/s1386-5056(02)00058-8.

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48

Sharma, Vijay Kumar, and Namita Mittal. "Exploiting Wikipedia API for Hindi-english Cross-language Information Retrieval." Procedia Computer Science 89 (2016): 434–40. http://dx.doi.org/10.1016/j.procs.2016.06.094.

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49

Savoy, Jacques. "Cross-language information retrieval: experiments based on CLEF 2000 corpora." Information Processing & Management 39, no. 1 (2003): 75–115. http://dx.doi.org/10.1016/s0306-4573(02)00018-3.

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50

Oard, Douglas W., and Philip Resnik. "Support for interactive document selection in cross-language information retrieval." Information Processing & Management 35, no. 3 (1999): 363–79. http://dx.doi.org/10.1016/s0306-4573(98)00066-1.

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