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

To see the other types of publications on this topic, follow the link: Cross-language information retrieval.

Author: Grafiati

Published: 4 June 2021

Last updated: 28 July 2024

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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 (August 30, 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 system, translation is based on query translation approach. The proposed system retrieves Marathi documents depending on matching terms in query. The performance of the proposed system is improved by query pre-processing and query expansion using WordNet.

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2

Gey, Fredric, Noriko Kando, and Carol Peters. "Cross language information retrieval." ACM SIGIR Forum 36, no. 2 (September 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 (January 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 (March 8, 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 (April 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 can help to improve the retrieval performance by 4%-7%, in terms of Mean Average Precision.

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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 (April 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 a user query where the query is a set of keywords. Information Retrieval can be classified into different classes such as Monolingual information retrieval, Bi-Lingual Information Retrieval, Multilingual information retrieval and Cross language information retrieval. This paper focuses on the various IR variants and techniques used in CLIR system. Further, based on available literature, a number of challenges and issues in CLIR have been identified and discussed. It gives an overview of the advantages, limitations, tools available in CLIR research. It also describes new application areas of CLIR such as medical, multimedia, question answering system etc. The need for exploring and building more specialized information system that enable speakers of an Odia language to discover valuable information beyond linguistic and cultural barriers. This study is aimed at building an experimental CLIR system between one of the under-resourced language (i.e. Odia) and one of the most commonly used online language (i.e. English) in future.

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8

Hedlund, Turid. "Dictionary-based cross-language information retrieval." ACM SIGIR Forum 38, no. 1 (July 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 (June 25, 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 (December 2, 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 (July 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 (February 28, 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 (November 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 (May 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 (September 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 (July 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 (June 2005): 115–36. http://dx.doi.org/10.1142/s0219427905001237.

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18

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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19

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

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20

Polajnar, Emil. "Using restricted CCA for cross-language information retrieval." Communications in Statistics - Simulation and Computation 46, no. 6 (January 27, 2017): 4618–26. http://dx.doi.org/10.1080/03610918.2015.1122054.

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21

Polajnar, Emil. "Using Lasso RCCA for cross-language information retrieval." Communications in Statistics - Simulation and Computation 47, no. 9 (September 5, 2017): 2739–48. http://dx.doi.org/10.1080/03610918.2017.1359287.

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22

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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23

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 documents and present to a user in the right order. This paper overcomes the first problem using an ontology model and we present a new ranking technique for a cross language information retrieval system (CLIR). Keyword weighting scheme in an ontology and document sections are introduced. Cosine similarity formula is modified to particularly support CLIR. The experimental results show the modified formula obtains more efficient ranking results than the existing method.

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24

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 (October 18, 2006): n/a. http://dx.doi.org/10.1002/meet.14504201106.

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25

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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26

Bajpai, Pratibha, Parul Verma, and Syed Q. Abbas. "English-Hindi Cross Language Information Retrieval System: Query Perspective." Journal of Computer Science 14, no. 5 (May 1, 2018): 705–13. http://dx.doi.org/10.3844/jcssp.2018.705.713.

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27

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 (December 2006): 323–59. http://dx.doi.org/10.1145/1236181.1236184.

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28

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

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29

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 (October 2008): 1925–32. http://dx.doi.org/10.1002/asi.20913.

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30

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 (September 11, 2008): 18–21. http://dx.doi.org/10.1002/bult.2007.bult1720330311.

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31

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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32

Kraaij, Wessel, Jian-Yun Nie, and Michel Simard. "Embedding Web-Based Statistical Translation Models in Cross-Language Information Retrieval." Computational Linguistics 29, no. 3 (September 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 in a retrieval model. In this article, we will investigate the problem of automatically mining parallel texts from the Web and different ways of integrating the translation models within the retrieval process. Our experiments on standard test collections for CLIR show that the Web-based translation models can surpass commercial MT systems in CLIR tasks. These results open the perspective of constructing a fully automatic query translation device for CLIR at a very low cost.

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33

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 readers to imagine. The lingering charm is endless, obscure, and meaningful, which is the traditional style of Japanese literature. It also focuses on the depoliticization of Japanese literature, that is, the relative separation between literature and politics. The subjectivity of Japanese writing, which goes beyond just conveying feelings but involves less objective description, is related to this. The process of cross-language information retrieval in foreign nations is primarily separated into three stages, according to the changes in research objects. Currently, adding a language conversion mechanism to a monolingual information retrieval system serves as the primary language information retrieval solution. Currently, nearly 40% of the global popularity of literature comes from Japan. In this article, the background and concept of cross-language information retrieval are introduced, and its types, system models, and several key cross-language information retrieval methods are explained, and some solutions to the factors influencing the cross-language information retrieval effect are suggested.

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34

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

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35

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 (May 2005): 457–73. http://dx.doi.org/10.1016/j.ipm.2004.06.008.

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36

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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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 literature data. Modal information retrieval is designed to combine high-level semantics with low-level visual capabilities in cross-modal information retrieval to improve the accuracy of information retrieval and then use experiments to verify the designed network model, and the result is that the model designed in this paper is more accurate than the traditional retrieval model, which is up to 90%.

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38

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 (January 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 Translation). We evaluate all proposals, comparing them to a ‘standard’ approach based on Latent Semantic Analysis, on a multilingual document clustering task. The evidence suggests that proper consideration of IT within IR is indeed called for: in all cases, our best results are achieved using the information-theoretic variations upon the standard approach. Furthermore, we show that different information-theoretic options can be combined for still better results. A key function of language is to encode and convey information, and contributions of IT to the field of CL can be traced back a number of decades. We think that our proposals help bring IR and CL more into line with one another. In our conclusion, we suggest that the fact that our proposals yield empirical improvements is not coincidental given that they increase the theoretical transparency of VSM approaches to IR; on the contrary, they help shed light on why aspects of these approaches work as they do.

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39

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 (April 25, 2020): 2320–25. http://dx.doi.org/10.30534/ijatcse/2020/214922020.

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40

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 (December 2002): 97–112. http://dx.doi.org/10.1016/s1386-5056(02)00058-8.

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41

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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42

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

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43

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

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44

Rahimi, Razieh, Ali Montazeralghaem, and Azadeh Shakery. "An axiomatic approach to corpus-based cross-language information retrieval." Information Retrieval Journal 23, no. 3 (April 9, 2020): 191–215. http://dx.doi.org/10.1007/s10791-020-09372-2.

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45

Seo, Hee-Cheol, Sang-Bum Kim, Hae-Chang Rim, and Sung-Hyon Myaeng. "Improving query translation in English–Korean cross-language information retrieval." Information Processing & Management 41, no. 3 (May 2005): 507–22. http://dx.doi.org/10.1016/j.ipm.2004.06.011.

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46

Oard, Douglas W., Daqing He, and Jianqiang Wang. "User-assisted query translation for interactive cross-language information retrieval." Information Processing & Management 44, no. 1 (January 2008): 181–211. http://dx.doi.org/10.1016/j.ipm.2006.12.009.

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47

Li, Bo, Eric Gaussier, and Dan Yang. "The Dilution/Concentration conditions for cross-language information retrieval models." Information Processing & Management 54, no. 2 (March 2018): 291–302. http://dx.doi.org/10.1016/j.ipm.2017.11.008.

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48

Rais, N. H., M. T. Abdullah, and R. A. Kadir. "Multiword Phrases Indexing for Malay-English Cross-Language Information Retrieval." Information Technology Journal 10, no. 8 (July 15, 2011): 1554–62. http://dx.doi.org/10.3923/itj.2011.1554.1562.

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49

Zhou, Dong, Mark Truran, Tim Brailsford, and Helen Ashman. "A Hybrid Technique for English-Chinese Cross Language Information Retrieval." ACM Transactions on Asian Language Information Processing 7, no. 2 (June 2008): 1–35. http://dx.doi.org/10.1145/1362782.1362784.

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50

ABU-SALEM, HANI, and PHILIP KA-FAI CHAN. "English-Arabic Cross-Language Information Retrieval Based on Parallel Documents." International Journal of Computer Processing of Languages 19, no. 01 (March 2006): 21–37. http://dx.doi.org/10.1142/s0219427906001372.

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