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Journal articles on the topic 'Cross lingual information retrieval'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Capstick, Joanne, Abdel Kader Diagne, Gregor Erbach, Hans Uszkoreit, Anne Leisenberg, and Manfred Leisenberg. "A system for supporting cross-lingual information retrieval." Information Processing & Management 36, no. 2 (2000): 275–89. http://dx.doi.org/10.1016/s0306-4573(99)00058-8.

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Feng, Kai, Lan Huang, Hao Xu, Kangping Wang, Wei Wei, and Rui Zhang. "Deep Multilabel Multilingual Document Learning for Cross-Lingual Document Retrieval." Entropy 24, no. 7 (2022): 943. http://dx.doi.org/10.3390/e24070943.

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Cross-lingual document retrieval, which aims to take a query in one language to retrieve relevant documents in another, has attracted strong research interest in the last decades. Most studies on this task start with cross-lingual comparisons at the word level and then represent documents via word embeddings, which leads to insufficient structure information. In this work, the cross-lingual comparison at the document level is achieved through the cross-lingual semantic space. Our method, MDL (deep multilabel multilingual document learning), leverages a six-layer fully connected network to proj

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Zuliarso, Eri, Retantyo Wardoyo, Sri Hartati, and Khabib Mustofa. "Indonesian-english cross-lingual legal ontology for information retrieval." International journal of Web & Semantic Technology 6, no. 4 (2015): 01–10. http://dx.doi.org/10.5121/ijwest.2015.6401.

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4

Gupta, Suneet Kumar, Amit Sinha, and Mradul Jain. "Cross Lingual Information Retrieval With SMT And Query Mining." Advanced Computing: An International Journal 2, no. 5 (2011): 33–39. http://dx.doi.org/10.5121/acij.2011.2504.

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Saad, Farag, and Andreas Nürnberger. "Overview of prior-art cross-lingual information retrieval approaches." World Patent Information 34, no. 4 (2012): 304–14. http://dx.doi.org/10.1016/j.wpi.2012.08.013.

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Sorg, P., and P. Cimiano. "Exploiting Wikipedia for cross-lingual and multilingual information retrieval." Data & Knowledge Engineering 74 (April 2012): 26–45. http://dx.doi.org/10.1016/j.datak.2012.02.003.

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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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Li, Juntao, Chang Liu, Jian Wang, et al. "Cross-Lingual Low-Resource Set-to-Description Retrieval for Global E-Commerce." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 8212–19. http://dx.doi.org/10.1609/aaai.v34i05.6335.

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With the prosperous of cross-border e-commerce, there is an urgent demand for designing intelligent approaches for assisting e-commerce sellers to offer local products for consumers from all over the world. In this paper, we explore a new task of cross-lingual information retrieval, i.e., cross-lingual set-to-description retrieval in cross-border e-commerce, which involves matching product attribute sets in the source language with persuasive product descriptions in the target language. We manually collect a new and high-quality paired dataset, where each pair contains an unordered product att

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Ghanbari, Elham, and Azadeh Shakery. "Query-dependent learning to rank for cross-lingual information retrieval." Knowledge and Information Systems 59, no. 3 (2018): 711–43. http://dx.doi.org/10.1007/s10115-018-1232-8.

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Lu, Chao, Chengzhi Zhang, and Daqing He. "Comparative analysis of book tags: a cross-lingual perspective." Electronic Library 34, no. 4 (2016): 666–82. http://dx.doi.org/10.1108/el-03-2015-0042.

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Purpose In the era of social media, users all over the world annotate books with social tags to express their preferences and interests. The purpose of this paper is to explore different tagging behaviours by analysing the book tags in different languages. Design/methodology/approach This investigation collected nearly 56,000 tags of 1,200 books from one Chinese and two English online bookmarking systems; it combined content analysis and machine-processing methods to evaluate the similarities and differences between different tagging systems from a cross-lingual perspective. Jaccard’s coeffici

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11

L, Divija, C. G. Hanisha Reddy, Rayudu Srishti, and Dr Surabhi Narayan. "Kannada to English Agricultural Cross-Lingual Retrieval: Enhancing Knowledge Access in Farming Practices." International Journal for Research in Applied Science and Engineering Technology 11, no. 9 (2023): 1672–77. http://dx.doi.org/10.22214/ijraset.2023.55879.

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Abstract: This paper presents the development and implementation of a specialized cross-lingual information retrieval system tailored for agriculture-related queries in the Kannada language. The primary objective of the system is to facilitate accurate translation of Kannada queries into English, the target language, and to retrieve relevant documents containing vital agricultural information. The proposed system addresses key challenges including incorporating effective query preprocessing techniques, designing an efficient document retrieval mechanism, and establishing optimal data indexing

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12

Kumar, Aarti, and Sujoy Das. "Dealing with Relevance Ranking in Cross-Lingual Cross-Script Text Reuse." International Journal of Information Retrieval Research 6, no. 1 (2016): 16–35. http://dx.doi.org/10.4018/ijirr.2016010102.

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Proliferation of multilingual content on the web has paved way for text reuse to get cross-lingual and also cross script. Identifying cross language text reuse becomes tougher if one considers cross-script less resourced languages. This paper focuses on identifying text reuse between English-Hindi news articles and improving their relevance ranking using two phases (i) Heuristic retrieval phase for reducing search space and (ii) post processing phase for improving the relevance ranking. Dictionary based strategy of Cross-Language Information Retrieval is used for heuristic retrieval and Parse

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Asthana, Amit, and Sanjay K. Dwivedi. "Exploring Snippets as a Dataset to Overcome Challenges in CLIR." ITM Web of Conferences 54 (2023): 01012. http://dx.doi.org/10.1051/itmconf/20235401012.

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Cross-lingual information retrieval (CLIR) is a challenging task that requires overcoming linguistic barriers to match user queries with relevant documents in different languages. One of the major challenges in CLIR is the lack of parallel corpora, which hinders the development of effective translation models. This challenge can be addressed using snippets as a dataset to train CLIR models. Snippets can be automatically extracted from various sources, such as search engine result pages and can provide a rich and diverse set of collections for cross-lingual information retrieval. This paper ini

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Et al., Chayapathi A. R. "CLOUD BASED MULTI-LANGUAGE INDEXING USING CROSS LINGUAL INFORMATION RETRIEVAL APPROACHES." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 1 (2021): 1283–93. http://dx.doi.org/10.17762/itii.v9i1.269.

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The exponential growth of data sizes created by digital media (video/audio/images), physicalsimulations, scientific instruments and web authoring joins the new growth of interest in cloud computing. The options for distribution and parallelization of information in clouds make the retrieval and storage processes very complicated, especially when faced with real-time data management. The quantity of Web Users getting access to data over Internet is expanding step by step. An enormous measure of data on Internet is accessible in various languages which could be accessed by anyone whenever. The I

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Zhang, Fuwei, Zhao Zhang, Xiang Ao, et al. "Mind the Gap: Cross-Lingual Information Retrieval with Hierarchical Knowledge Enhancement." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (2022): 4345–53. http://dx.doi.org/10.1609/aaai.v36i4.20355.

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Cross-Lingual Information Retrieval (CLIR) aims to rank the documents written in a language different from the user’s query. The intrinsic gap between different languages is an essential challenge for CLIR. In this paper, we introduce the multilingual knowledge graph (KG) to the CLIR task due to the sufficient information of entities in multiple languages. It is regarded as a “silver bullet” to simultaneously perform explicit alignment between queries and documents and also broaden the representations of queries. And we propose a model named CLIR with HIerarchical Knowledge Enhancement (HIKE)

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Kishida, Kazuaki, Kuang-hua Chen, Sukhoon Lee, et al. "Cross-lingual information retrieval (CLIR) task at the NTCIR workshop 3." ACM SIGIR Forum 38, no. 1 (2004): 17–20. http://dx.doi.org/10.1145/986278.986281.

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17

Chi, H. V. T., D. L. Anh, N. L. Thanh, and D. Dinh. "English-Vietnamese Cross-Lingual Paraphrase Identification Using MT-DNN." Engineering, Technology & Applied Science Research 11, no. 5 (2021): 7598–604. http://dx.doi.org/10.48084/etasr.4300.

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Paraphrase identification is a crucial task in natural language understanding, especially in cross-language information retrieval. Nowadays, Multi-Task Deep Neural Network (MT-DNN) has become a state-of-the-art method that brings outstanding results in paraphrase identification [1]. In this paper, our proposed method based on MT-DNN [2] to detect similarities between English and Vietnamese sentences, is proposed. We changed the shared layers of the original MT-DNN from original the BERT [3] to other pre-trained multi-language models such as M-BERT [3] or XLM-R [4] so that our model could work

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18

Pachpande, Suhas D., and Parag U. Bhalchandra. "Cross Language Information Retrieval based on Automatic Query Translation for Marathi Documents." International Journal for Research in Applied Science and Engineering Technology 11, no. 9 (2023): 394–400. http://dx.doi.org/10.22214/ijraset.2023.55658.

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Abstract: The current research article explores the realm of Cross Language Information Retrieval (CLIR) and its significance in the digital age. It addresses the challenges faced in CLIR, including lexical and semantic disparities, the scarcity of parallel corpora, cultural nuances, and more. The article discusses innovative solutions encompassing Machine Translation, Query Expansion, Cross-Lingual Word Embeddings, and Multilingual Information Retrieval Models to enhance CLIR's effectiveness. Furthermore, it sheds light on Information Retrieval Models, such as the Boolean Model, Vector Space

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19

Schulz, Stefan, and Udo Hahn. "Morpheme-based, cross-lingual indexing for medical document retrieval." International Journal of Medical Informatics 58-59 (September 2000): 87–99. http://dx.doi.org/10.1016/s1386-5056(00)00078-2.

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20

Chau, Rowena, and Chung-Hsing Yeh. "A multilingual text mining approach to web cross-lingual text retrieval." Knowledge-Based Systems 17, no. 5-6 (2004): 219–27. http://dx.doi.org/10.1016/j.knosys.2004.04.001.

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21

S N, Muralikrishna, Raghurama Holla, Harivinod N, and Raghavendra Ganiga. "Cross-Lingual Short-Text Semantic Similarity for Kannada–English Language Pair." Computers 13, no. 9 (2024): 236. http://dx.doi.org/10.3390/computers13090236.

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Analyzing the semantic similarity of cross-lingual texts is a crucial part of natural language processing (NLP). The computation of semantic similarity is essential for a variety of tasks such as evaluating machine translation systems, quality checking human translation, information retrieval, plagiarism checks, etc. In this paper, we propose a method for measuring the semantic similarity of Kannada–English sentence pairs that uses embedding space alignment, lexical decomposition, word order, and a convolutional neural network. The proposed method achieves a maximum correlation of 83% with hum

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22

Zhou, Dong, Séamus Lawless, Xuan Wu, Wenyu Zhao, and Jianxun Liu. "A study of user profile representation for personalized cross-language information retrieval." Aslib Journal of Information Management 68, no. 4 (2016): 448–77. http://dx.doi.org/10.1108/ajim-06-2015-0091.

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Purpose – With an increase in the amount of multilingual content on the World Wide Web, users are often striving to access information provided in a language of which they are non-native speakers. The purpose of this paper is to present a comprehensive study of user profile representation techniques and investigate their use in personalized cross-language information retrieval (CLIR) systems through the means of personalized query expansion. Design/methodology/approach – The user profiles consist of weighted terms computed by using frequency-based methods such as tf-idf and BM25, as well as va

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23

Shakery, Azadeh, and ChengXiang Zhai. "Leveraging comparable corpora for cross-lingual information retrieval in resource-lean language pairs." Information Retrieval 16, no. 1 (2012): 1–29. http://dx.doi.org/10.1007/s10791-012-9194-z.

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24

Ehsan, Nava, and Azadeh Shakery. "Candidate document retrieval for cross-lingual plagiarism detection using two-level proximity information." Information Processing & Management 52, no. 6 (2016): 1004–17. http://dx.doi.org/10.1016/j.ipm.2016.04.006.

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25

Du, Lin, Yibo Zhang, Le Sun, and Yufang Sun. "The application of the comparable corpora in Chinese-English Cross-Lingual Information Retrieval." Journal of Computer Science and Technology 16, no. 4 (2001): 351–58. http://dx.doi.org/10.1007/bf02948983.

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26

Martín-Valdivia, M. T., F. Martínez-Santiago, and L. A. Ureña-López. "Merging Strategy for Cross-Lingual Information Retrieval Systems based on Learning Vector Quantization." Neural Processing Letters 22, no. 2 (2005): 149–61. http://dx.doi.org/10.1007/s11063-005-2659-y.

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27

Lam, Wai, Ki Chan, Dragomir Radev, Horacio Saggion, and Simone Teufel. "Context-based generic cross-lingual retrieval of documents and automated summaries." Journal of the American Society for Information Science and Technology 56, no. 2 (2004): 129–39. http://dx.doi.org/10.1002/asi.20104.

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28

Doncel, Víctor Rodríguez, and Elena Montiel Ponsoda. "LYNX: Towards a Legal Knowledge Graph for Multilingual Europe." Law in Context. A Socio-legal Journal 37, no. 1 (2020): 175–78. http://dx.doi.org/10.26826/law-in-context.v37i1.129.

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Lynx is an innovation project in Europe whose objective is to develop services for legal compliance. A legal knowledge graph is built over multilingual, multijurisdictional documents using semantic web technologies. A collection of services implementing natural language techniques enables better legal information retrieval, cross-lingual answering of questions and information discovery. Three use cases are discussed, as well as the overall impact of the project.

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Wumaier, Aishan, Cuiyun Xu, Zaokere Kadeer, et al. "A Neural-Network-Based Approach to Chinese–Uyghur Organization Name Translation." Information 11, no. 10 (2020): 492. http://dx.doi.org/10.3390/info11100492.

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The recognition and translation of organization names (ONs) is challenging due to the complex structures and high variability involved. ONs consist not only of common generic words but also names, rare words, abbreviations and business and industry jargon. ONs are a sub-class of named entity (NE) phrases, which convey key information in text. As such, the correct translation of ONs is critical for machine translation and cross-lingual information retrieval. The existing Chinese–Uyghur neural machine translation systems have performed poorly when applied to ON translation tasks. As there are no

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Mishra, Dr Rudra Prasad. "Transliteration: A Magnetic Analysis." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (2021): 85–86. http://dx.doi.org/10.22214/ijraset.2021.38742.

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Abstract: Machine transliteration is an important problem in an increasingly multilingual world as it plays a critical role in many downstream applications such as machine translation or cross-lingual information retrieval systems. There is now a vast amount of information accessible via the Internet where a lot of regional and cultural information is put on the World Wide Web in different languages and scripts. There are more that six thousand living languages in the world. Adding to the diversity is the fact that some languages are written in different scripts in different regions of the wor

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Novak, Erik, Luka Bizjak, Dunja Mladenić, and Marko Grobelnik. "Why is a document relevant? Understanding the relevance scores in cross-lingual document retrieval." Knowledge-Based Systems 244 (May 2022): 108545. http://dx.doi.org/10.1016/j.knosys.2022.108545.

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Sharma, Vijay, and Namita Mittal. "Refined stop-words and morphological variants solutions applied to Hindi-English cross-lingual information retrieval." Journal of Intelligent & Fuzzy Systems 36, no. 3 (2019): 2219–27. http://dx.doi.org/10.3233/jifs-169933.

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Zhang, Ying, Phil Vines, and Justin Zobel. "Chinese OOV translation and post-translation query expansion in chinese--english cross-lingual information retrieval." ACM Transactions on Asian Language Information Processing 4, no. 2 (2005): 57–77. http://dx.doi.org/10.1145/1105696.1105697.

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34

Chen, Dr Mingyu L., and Muhammad Siddiqui. "CODE-SWITCHED RELATION EXTRACTION: A NOVEL DATASET AND TRAINING METHODOLOGY." International Journal of Modern Computer Science and IT Innovations 2, no. 2 (2025): 1–9. https://doi.org/10.55640/ijmcsit-v02i02-01.

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Relation Extraction (RE) is a fundamental task in Natural Language Processing (NLP) crucial for constructing knowledge graphs and enhancing information retrieval. While significant progress has been made in monolingual and cross-lingual RE, the unique challenges posed by code-switched (mix-lingual) text remain largely underexplored due to a scarcity of dedicated datasets and tailored methodologies. This paper introduces a novel, large-scale dataset specifically designed for code-switched relation extraction. Furthermore, we propose an effective training methodology tailored to capture the comp

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35

Costa-jussà, Marta R., Srinivas Bangalore, Patrik Lambert, Lluís Màrquez, and Elena Montiel-Ponsoda. "Introduction to the Special Issue on Cross-Language Algorithms and Applications." Journal of Artificial Intelligence Research 55 (January 12, 2016): 1–15. http://dx.doi.org/10.1613/jair.5022.

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With the increasingly global nature of our everyday interactions, the need for multilin- gual technologies to support efficient and effective information access and communication cannot be overemphasized. Computational modeling of language has been the focus of Natural Language Processing, a subdiscipline of Artificial Intelligence. One of the current challenges for this discipline is to design methodologies and algorithms that are cross- language in order to create multilingual technologies rapidly. The goal of this JAIR special issue on Cross-Language Algorithms and Applications (CLAA) is to

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36

M.Sapkal, Kunal, Vinayak R.Gharge, Sanika J.Kadam, Rutuja N.Mulik, and Prof Vaibhav U. Bhosale. "VEDA-VISION GPT-AN AI-POWERED MULTILINGUAL DOCUMENT PROCESSING AND INTERACTION PLATFORM." International Journal of Engineering Applied Sciences and Technology 09, no. 05 (2024): 129–34. http://dx.doi.org/10.33564/ijeast.2024.v09i05.015.

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VEDA-VISIONGPT innovates multilingual document handling by unifying text recognition, language translation, and retrieval-augmented generation with interactive AI. This ground breaking platform extracts content from all types of documents in diverse Indian language sources, renders translations across more than 15 native tongues, and facilitates natural language queries. Employing advanced OCR and AI technologies, it offers comprehensive multilingual document management. Enhanced text analytics enable clear, logical information extraction. Aimed at government, legal, and academic sectors requi

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Azarbonyad, Hosein, Azadeh Shakery, and Heshaam Faili. "A learning to rank approach for cross-language information retrieval exploiting multiple translation resources." Natural Language Engineering 25, no. 3 (2019): 363–84. http://dx.doi.org/10.1017/s1351324919000032.

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AbstractCross-language information retrieval (CLIR), finding information in one language in response to queries expressed in another language, has attracted much attention due to the explosive growth of multilingual information in the World Wide Web. One important issue in CLIR is how to apply monolingual information retrieval (IR) methods in cross-lingual environments. Recently, learning to rank (LTR) approach has been successfully employed in different IR tasks. In this paper, we use LTR for CLIR. In order to adapt monolingual LTR techniques in CLIR and pass the barrier of language differenc

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S, Subi, Shanthini B, SilpaRaj M, Shekar K, Keerthana G, and Anitha R. "Natural Language Processing Techniques for Information Retrieval Enhancing Search Engines with Semantic Understanding." ITM Web of Conferences 76 (2025): 05013. https://doi.org/10.1051/itmconf/20257605013.

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This paper investigates new Natural Language Processing (NLP) methods which seek to improve information retrieval systems via semantic knowledge and focuses on enhancing search engines. The proposed ideas focus on reducing the size of the model (one of the biggest problems with large models), training it on domain-specific knowledge (the right knowledge is important for the real application) and ways to efficiently deal with unstructured data (this is also a key issue against NLP frameworks). The study highlights the need for hybrid models that combine generalization and specificity, fast algo

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Khwileh, Ahmad, Debasis Ganguly, and Gareth J. F. Jones. "Utilisation of Metadata Fields and Query Expansion in Cross-Lingual Search of User-Generated Internet Video." Journal of Artificial Intelligence Research 55 (January 27, 2016): 249–81. http://dx.doi.org/10.1613/jair.4775.

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Recent years have seen significant efforts in the area of Cross Language Information Retrieval (CLIR) for text retrieval. This work initially focused on formally published content, but more recently research has begun to concentrate on CLIR for informal social media content. However, despite the current expansion in online multimedia archives, there has been little work on CLIR for this content. While there has been some limited work on Cross-Language Video Retrieval (CLVR) for professional videos, such as documentaries or TV news broadcasts, there has to date, been no significant investigatio

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Wang, Jiayi, Qiwen Zhao, and Yue Xi. "Cross-lingual Search Intent Understanding Framework Based on Multi-modal User Behavior." International Journal of Language Studies (ISSN : 3078 - 2244) 1, no. 2 (2024): 65–73. https://doi.org/10.60087/ijls.v1.n2.007.

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This paper proposes a novel cross-lingual search intent understanding framework leveraging multi-modal user behavior analysis. With the increasing complexity of network traffic and the diversity of user behaviors across languages, traditional approaches often struggle to capture and interpret user search intent in multilingual contexts accurately. Our framework integrates multiple behavioral signals, including query patterns, click sequences, and temporal dynamics, through a sophisticated neural tensor network architecture. The system employs a dual-encoder structure with shared parameters to

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Lakshmi, Jammulamadaka, Reddy E.Srinivasa, and J.Syambabu. "A Comprehensive Survey on Chatbot Technologies for Advanced Information Processing." International Journal for Modern Trends in Science and Technology 11, no. 06 (2025): 05–24. https://doi.org/10.5281/zenodo.15576813.

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<em>Chatbots have emerged as a crucial component of modern human-computer interaction, transforming the way individuals and organizations access information and engage in communication. With advancements in artificial intelligence, chatbots now perform a diverse array of tasks, including sentiment analysis, multilingual and cross-lingual natural language processing, information retrieval, and document classification. Such improvements allow chatbots&ensp;to deliver services that are more individualized, supportive of broader demographics, and effective in various sectors like customer support,

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Taghizadeh, Nasrin, and Hesham Faili. "Automatic Wordnet Development for Low-Resource Languages using Cross-Lingual WSD." Journal of Artificial Intelligence Research 56 (May 20, 2016): 61–87. http://dx.doi.org/10.1613/jair.4968.

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‎Wordnets are an effective resource for natural language processing and information retrieval‎, ‎especially for semantic processing and meaning related tasks‎. ‎So far‎, ‎wordnets have been constructed for many languages‎. ‎However‎, ‎the automatic development of wordnets for low-resource languages has not been well studied‎. ‎In this paper‎, ‎an Expectation-Maximization algorithm is used to create high quality and large scale wordnets for poor-resource languages‎. ‎The proposed method benefits from possessing cross-lingual word sense disambiguation and develops a wordnet by only using a bi-li

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Zeng, Jing, and Chung-hong Chan. "A cross-national diagnosis of infodemics: comparing the topical and temporal features of misinformation around COVID-19 in China, India, the US, Germany and France." Online Information Review 45, no. 4 (2021): 709–28. http://dx.doi.org/10.1108/oir-09-2020-0417.

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PurposeThis study empirically investigates how the COVID-infodemic manifests differently in different languages and in different countries. This paper focuses on the topical and temporal features of misinformation related to COVID-19 in five countries.Design/methodology/approachCOVID-related misinformation was retrieved from 4,487 fact-checked articles. A novel approach to conducting cross-lingual topic extraction was applied. The rectr algorithm, empowered by aligned word-embedding, was utilised. To examine how the COVID-infodemic interplays with the pandemic, a time series analysis was used

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Choi, YooChan. "Korean to English Patent Automatic Translation (K2E-PAT) and cross lingual retrieval on KIPRIS." World Patent Information 31, no. 2 (2009): 135–36. http://dx.doi.org/10.1016/j.wpi.2008.09.005.

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45

Amit Asthana. "Approach to Handle Compound Out of Vocabulary Words in Hindi Web Queries." Journal of Information Systems Engineering and Management 10, no. 13s (2025): 761–69. https://doi.org/10.52783/jisem.v10i13s.2158.

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Introduction: Detection and handling Out of Vocabulary (OOV) words in information retrieval is a challenging task. This problem may become more challenging in case of Cross-Lingual Information Retrieval (CLIR) due to the complications with query translation. Compound Hindi OOV word problem has been less discussed in literature and no appropriate solution has been provided to overcome the issue in CLIR with web queries having such words. These words if not identified, may restrict to understand the proper meaning. Objectives: The objective of this paper is to understand the impact and to handle

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46

Hao Yang. "Multilingual Information Retrieval Using Graph Neural Networks: Practical Applications in English Translation." Journal of Electrical Systems 20, no. 6s (2024): 1729–39. http://dx.doi.org/10.52783/jes.3091.

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Multilingual information retrieval using graph neural networks offers practical applications in English translation by leveraging advanced computational models to enhance the efficiency and accuracy of cross-lingual search and translation tasks. By representing textual data as graphs and utilizing graph neural networks (GNNs), this approach captures intricate relationships between words and phrases across different languages, enabling more effective language understanding and translation. GNNs can learn complex linguistic structures and semantic similarities from multilingual corpora, facilita

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Meuschke, Norman, and Bela Gipp. "State of the Art in Detecting Academic Plagiarism." International Journal for Educational Integrity 9, no. 1 (2013): 50–71. https://doi.org/10.5281/zenodo.3482941.

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The problem of academic plagiarism has been present for centuries. Yet, the widespread dissemination of information technology, including the internet, made plagiarising much easier. Consequently, methods and systems aiding in the detection of plagiarism have attracted much research within the last two decades. Researchers proposed a variety of solutions, which we will review comprehensively in this article. Available detection systems use sophisticated and highly efficient character-based text comparisons, which can reliably identify verbatim and moderately disguised copies. Automatically det

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Huang, Yanan, and Yuji Miao. "Selection of the Most Relevant Online English Semantic Art Translation in Cross-Lingual Information Retrieval based on Speech Signal Analysis Model." International Journal of Arts and Technology 13, no. 3 (2021): 1. http://dx.doi.org/10.1504/ijart.2021.10043418.

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Miao, Yuji, and Yanan Huang. "Selection of the most relevant online English semantic art translation in cross-lingual information retrieval based on speech signal analysis model." International Journal of Arts and Technology 13, no. 3 (2021): 200. http://dx.doi.org/10.1504/ijart.2021.120761.

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Tamchyna, Aleš, Ondřej Dušek, Rudolf Rosa, and Pavel Pecina. "MTMonkey: A Scalable Infrastructure for a Machine Translation Web Service." Prague Bulletin of Mathematical Linguistics 100, no. 1 (2013): 31–40. http://dx.doi.org/10.2478/pralin-2013-0009.

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Abstract We present a web service which handles and distributes JSON-encoded HTTP requests for machine translation (MT) among multiple machines running an MT system, including text pre- and post-processing. It is currently used to provide MT between several languages for cross-lingual information retrieval in the EU FP7 Khresmoi project. The software consists of an application server and remote workers which handle text processing and communicate translation requests to MT systems. The communication between the application server and the workers is based on the XML-RPC protocol. We present the

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