Relevant bibliographies by topics / Cross lingual information retrieval

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Cross lingual information retrieval'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 June 2024

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Journal articles on the topic "Cross lingual information retrieval"

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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 (March 2000): 275–89. http://dx.doi.org/10.1016/s0306-4573(99)00058-8.

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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 (October 30, 2015): 01–10. http://dx.doi.org/10.5121/ijwest.2015.6401.

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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 (September 30, 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 (December 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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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 (July 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 project cross-lingual documents into a shared semantic space. The semantic distances can be calculated when the cross-lingual documents are transformed into embeddings in semantic space. The supervision signals are automatically extracted from the data and then used to construct the semantic space via a linear classifier. The ambiguity of manual labels could be avoided and the multilabel supervision signals can be acquired instead of a single label. The representation of the semantic space is enriched by multilabel supervision signals, which improves the discriminative ability of the embeddings. The MDL is easy to extend to other fields since it does not depend on specific data. Furthermore, MDL is more efficient than the models training all languages jointly, since each language is trained individually. Experiments on Wikipedia data showed that the proposed method outperforms the state-of-the-art cross-lingual document retrieval methods.
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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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Ghanbari, Elham, and Azadeh Shakery. "Query-dependent learning to rank for cross-lingual information retrieval." Knowledge and Information Systems 59, no. 3 (July 4, 2018): 711–43. http://dx.doi.org/10.1007/s10115-018-1232-8.

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Li, Juntao, Chang Liu, Jian Wang, Lidong Bing, Hongsong Li, Xiaozhong Liu, Dongyan Zhao, and Rui Yan. "Cross-Lingual Low-Resource Set-to-Description Retrieval for Global E-Commerce." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 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 attribute set in the source language and an informative product description in the target language. As the dataset construction process is both time-consuming and costly, the new dataset only comprises of 13.5k pairs, which is a low-resource setting and can be viewed as a challenging testbed for model development and evaluation in cross-border e-commerce. To tackle this cross-lingual set-to-description retrieval task, we propose a novel cross-lingual matching network (CLMN) with the enhancement of context-dependent cross-lingual mapping upon the pre-trained monolingual BERT representations. Experimental results indicate that our proposed CLMN yields impressive results on the challenging task and the context-dependent cross-lingual mapping on BERT yields noticeable improvement over the pre-trained multi-lingual BERT model.
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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 (August 1, 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 coefficient was adopted to evaluate the similarity level. Findings The results show that the similarity between mono-lingual tags of the same books is higher than that of cross-lingual tags in different systems and the similarity between tags of books written for specialties is higher than that of books written for the general public. Research limitations/implications Those who have more in common annotate books with more similar tags. The similarity between users in tagging systems determines the similarity of the tag sets. Practical implications The results and conclusion of this study will benefit users’ cross-lingual information retrieval and cross-lingual book recommendation for online bookmarking systems. Originality/value This study may be one of the first to compare cross-lingual tags. Its methodology can be applied to tag comparison between any two languages. The insights of this study will help develop cross-lingual tagging systems and improve information retrieval.
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Dissertations / Theses on the topic "Cross lingual information retrieval"

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Liu, Qing. "A Neural Approach to Cross-Lingual Information Retrieval." Research Showcase @ CMU, 2018. http://repository.cmu.edu/theses/135.

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With the rapid growth of world-wide information accessibility, cross-language information retrieval (CLIR) has become a prominent concern for search engines. Traditional CLIR technologies require special purpose components and need high quality translation knowledge (e.g. machine readable dictionaries, machine translation systems) and careful tuning to achieve high ranking performance. However, with the help of a neural network architecture, it’s possible to solve CLIR problem without extra tuning or special components. This work proposes a bilingual training approach, a neural CLIR solution allowing automatic learning of translation relationships from noisy translation knowledge. External sources of translation knowledge are used to generate bilingual training data then the bilingual training data is fed into a kernel based neural ranking model. During the end-to-end training, word embeddings are tuned to preserve translation relationships between bilingual word pairs and also tailored for the ranking task. In experiments we show that the bilingual training approach outperforms traditional CLIR techniques given the same external translation knowledge source and it’s able to yield ranking results as good as that of a monolingual information retrieval system. In experiments we investigate the source of effectiveness for our neural CLIR approach by analyzing the pattern of trained word embeddings. Also, possible methods to further improve performance are explored in experiments, including cleaning training data by removing ambiguous training queries, exploring whether more training data will improve the performance by learning the relationship between training dataset size and model performance, and investigating the affect of English queries’ text-transform in training data. Lastly, we design an experiment that analyzes the quality of testing query translation to quantify the model performance in a real testing scenario where model takes manually written English queries as input.
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陸穎剛 and Wing-kong Luk. "Concept space approach for cross-lingual information retrieval." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B30147724.

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Luk, Wing-kong. "Concept space approach for cross-lingual information retrieval /." Hong Kong : University of Hong Kong, 2000. http://sunzi.lib.hku.hk/hkuto/record.jsp?B2275345X.

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Boynuegri, Akif. "Cross-lingual Information Retrieval On Turkish And English Texts." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12611903/index.pdf.

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In this thesis, cross-lingual information retrieval (CLIR) approaches are comparatively evaluated for Turkish and English texts. As a complementary study, knowledge-based methods for word sense disambiguation (WSD), which is one of the most important parts of the CLIR studies, are compared for Turkish words. Query translation and sense indexing based CLIR approaches are used in this study. In query translation approach, we use automatic and manual word sense disambiguation methods and Google translation service during translation of queries. In sense indexing based approach, documents are indexed according to meanings of words instead of words themselves. Retrieval of documents is performed according to meanings of the query words as well. During the identification of intended meaning of query terms, manual and automatic word sense disambiguation methods are used and compared to each other. Knowledge based WSD methods that use different gloss enrichment techniques are compared for Turkish words. Turkish WordNet is used as a primary knowledge base and English WordNet and Turkish Wikipedia are employed as enrichment resources. Meanings of words are more clearly identified by using semantic relations defined in WordNets and Turkish Wikipedia. Also, during calculation of semantic relatedness of senses, cosine similarity metric is used as an alternative metric to word overlap count. Effects of using cosine similarity metric are observed for each WSD methods that use different knowledge bases.
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Wang, Xinkai. "Chinese-English cross-lingual information retrieval in biomedicine using ontology-based query expansion." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/chineseenglish-crosslingual-information-retrieval-in-biomedicine-using-ontologybased-query-expansion(1b7443d3-3baf-402b-83bb-f45e78876404).html.

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In this thesis, we propose a new approach to Chinese-English Biomedical cross-lingual information retrieval (CLIR) using query expansion based on the eCMeSH Tree, a Chinese-English ontology extended from the Chinese Medical Subject Headings (CMeSH) Tree. The CMeSH Tree is not designed for information retrieval (IR), since it only includes heading terms and has no term weighting scheme for these terms. Therefore, we design an algorithm, which employs a rule-based parsing technique combined with the C-value term extraction algorithm and a filtering technique based on mutual information, to extract Chinese synonyms for the corresponding heading terms. We also develop a term-weighting mechanism. Following the hierarchical structure of CMeSH, we extend the CMeSH Tree to the eCMeSH Tree with synonymous terms and their weights. We propose an algorithm to implement CLIR using the eCMeSH Tree terms to expand queries. In order to evaluate the retrieval improvements obtained from our approach, the results of the query expansion based on the eCMeSH Tree are individually compared with the results of the experiments of query expansion using the CMeSH Tree terms, query expansion using pseudo-relevance feedback, and document translation. We also evaluate the combinations of these three approaches. This study also investigates the factors which affect the CLIR performance, including a stemming algorithm, retrieval models, and word segmentation.
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Ahmed, Farag [Verfasser], and Andreas [Akademischer Betreuer] Nürnberger. "Meaning refinement to improve cross-lingual information retrieval / Farag Ahmed. Betreuer: Andreas Nürnberger." Magdeburg : Universitätsbibliothek, 2012. http://d-nb.info/1047596040/34.

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Ahmed, Farag Verfasser], and Andreas [Akademischer Betreuer] [Nürnberger. "Meaning refinement to improve cross-lingual information retrieval / Farag Ahmed. Betreuer: Andreas Nürnberger." Magdeburg : Universitätsbibliothek, 2012. http://nbn-resolving.de/urn:nbn:de:gbv:ma9:1-730.

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Tang, Ling-Xiang. "Link discovery for Chinese/English cross-language web information retrieval." Thesis, Queensland University of Technology, 2012. https://eprints.qut.edu.au/58416/1/Ling-Xiang_Tang_Thesis.pdf.

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Nowadays people heavily rely on the Internet for information and knowledge. Wikipedia is an online multilingual encyclopaedia that contains a very large number of detailed articles covering most written languages. It is often considered to be a treasury of human knowledge. It includes extensive hypertext links between documents of the same language for easy navigation. However, the pages in different languages are rarely cross-linked except for direct equivalent pages on the same subject in different languages. This could pose serious difficulties to users seeking information or knowledge from different lingual sources, or where there is no equivalent page in one language or another. In this thesis, a new information retrieval task—cross-lingual link discovery (CLLD) is proposed to tackle the problem of the lack of cross-lingual anchored links in a knowledge base such as Wikipedia. In contrast to traditional information retrieval tasks, cross language link discovery algorithms actively recommend a set of meaningful anchors in a source document and establish links to documents in an alternative language. In other words, cross-lingual link discovery is a way of automatically finding hypertext links between documents in different languages, which is particularly helpful for knowledge discovery in different language domains. This study is specifically focused on Chinese / English link discovery (C/ELD). Chinese / English link discovery is a special case of cross-lingual link discovery task. It involves tasks including natural language processing (NLP), cross-lingual information retrieval (CLIR) and cross-lingual link discovery. To justify the effectiveness of CLLD, a standard evaluation framework is also proposed. The evaluation framework includes topics, document collections, a gold standard dataset, evaluation metrics, and toolkits for run pooling, link assessment and system evaluation. With the evaluation framework, performance of CLLD approaches and systems can be quantified. This thesis contributes to the research on natural language processing and cross-lingual information retrieval in CLLD: 1) a new simple, but effective Chinese segmentation method, n-gram mutual information, is presented for determining the boundaries of Chinese text; 2) a voting mechanism of name entity translation is demonstrated for achieving a high precision of English / Chinese machine translation; 3) a link mining approach that mines the existing link structure for anchor probabilities achieves encouraging results in suggesting cross-lingual Chinese / English links in Wikipedia. This approach was examined in the experiments for better, automatic generation of cross-lingual links that were carried out as part of the study. The overall major contribution of this thesis is the provision of a standard evaluation framework for cross-lingual link discovery research. It is important in CLLD evaluation to have this framework which helps in benchmarking the performance of various CLLD systems and in identifying good CLLD realisation approaches. The evaluation methods and the evaluation framework described in this thesis have been utilised to quantify the system performance in the NTCIR-9 Crosslink task which is the first information retrieval track of this kind.
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Asian, Jelita, and jelitayang@gmail com. "Effective Techniques for Indonesian Text Retrieval." RMIT University. Computer Science and Information Technology, 2007. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080110.084651.

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The Web is a vast repository of data, and information on almost any subject can be found with the aid of search engines. Although the Web is international, the majority of research on finding of information has a focus on languages such as English and Chinese. In this thesis, we investigate information retrieval techniques for Indonesian. Although Indonesia is the fourth most populous country in the world, little attention has been given to search of Indonesian documents. Stemming is the process of reducing morphological variants of a word to a common stem form. Previous research has shown that stemming is language-dependent. Although several stemming algorithms have been proposed for Indonesian, there is no consensus on which gives better performance. We empirically explore these algorithms, showing that even the best algorithm still has scope for improvement. We propose novel extensions to this algorithm and develop a new Indonesian stemmer, and show that these can improve stemming correctness by up to three percentage points; our approach makes less than one error in thirty-eight words. We propose a range of techniques to enhance the performance of Indonesian information retrieval. These techniques include: stopping; sub-word tokenisation; and identification of proper nouns; and modifications to existing similarity functions. Our experiments show that many of these techniques can increase retrieval performance, with the highest increase achieved when we use grams of size five to tokenise words. We also present an effective method for identifying the language of a document; this allows various information retrieval techniques to be applied selectively depending on the language of target documents. We also address the problem of automatic creation of parallel corpora --- collections of documents that are the direct translations of each other --- which are essential for cross-lingual information retrieval tasks. Well-curated parallel corpora are rare, and for many languages, such as Indonesian, do not exist at all. We describe algorithms that we have developed to automatically identify parallel documents for Indonesian and English. Unlike most current approaches, which consider only the context and structure of the documents, our approach is based on the document content itself. Our algorithms do not make any prior assumptions about the documents, and are based on the Needleman-Wunsch algorithm for global alignment of protein sequences. Our approach works well in identifying Indonesian-English parallel documents, especially when no translation is performed. It can increase the separation value, a measure to discriminate good matches of parallel documents from bad matches, by approximately ten percentage points. We also investigate the applicability of our identification algorithms for other languages that use the Latin alphabet. Our experiments show that, with minor modifications, our alignment methods are effective for English-French, English-German, and French-German corpora, especially when the documents are not translated. Our technique can increase the separation value for the European corpus by up to twenty-eight percentage points. Together, these results provide a substantial advance in understanding techniques that can be applied for effective Indonesian text retrieval.
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Saad, Motaz. "Fouille de documents et d'opinions multilingue." Thesis, Université de Lorraine, 2015. http://www.theses.fr/2015LORR0003/document.

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L’objectif de cette thèse est d’étudier les sentiments dans les documents comparables. Premièrement, nous avons recueillis des corpus comparables en anglais, français et arabe de Wikipédia et d’Euronews, et nous avons aligné ces corpus au niveau document. Nous avons en plus collecté des documents d’informations des agences de presse locales et étrangères dans les langues anglaise et arabe. Les documents en anglais ont été recueillis du site de la BBC, ceux en arabe du site d’Al-Jazzera. Deuxièmement, nous avons présenté une mesure de similarité cross-linguistique des documents dans le but de récupérer et aligner automatiquement les documents comparables. Ensuite, nous avons proposé une méthode d’annotation cross-linguistique en termes de sentiments, afin d’étiqueter les documents source et cible avec des sentiments. Enfin, nous avons utilisé des mesures statistiques pour comparer l’accord des sentiments entre les documents comparables source et cible. Les méthodes présentées dans cette thèse ne dépendent pas d’une paire de langue bien déterminée, elles peuvent être appliquées sur toute autre couple de langue
The aim of this thesis is to study sentiments in comparable documents. First, we collect English, French and Arabic comparable corpora from Wikipedia and Euronews, and we align each corpus at the document level. We further gather English-Arabic news documents from local and foreign news agencies. The English documents are collected from BBC website and the Arabic documents are collected from Al-jazeera website. Second, we present a cross-lingual document similarity measure to automatically retrieve and align comparable documents. Then, we propose a cross-lingual sentiment annotation method to label source and target documents with sentiments. Finally, we use statistical measures to compare the agreement of sentiments in the source and the target pair of the comparable documents. The methods presented in this thesis are language independent and they can be applied on any language pair
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Books on the topic "Cross lingual information retrieval"

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Japan) NTCIR Workshop Meeting (7th 2008 Tokyo. NTCIR Workshop 7 Meeting: Proceedings of the 7th NTCIR Workshop Meeting on evaluation of information access technologies : information retrieval, question answering and cross-lingual information access. Tokyo: National Institute of Informatics, 2008.

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Nie, Jian-Yun. Cross-Language Information Retrieval. Cham: Springer International Publishing, 2010. http://dx.doi.org/10.1007/978-3-031-02138-1.

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Grefenstette, Gregory, ed. Cross-Language Information Retrieval. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5661-9.

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Cross-Language Information Retrieval. Boston, MA: Springer US, 1998.

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1956-, Grefenstette Gregory, ed. Cross-language information retrieval. Boston, MA: Kluwer Academic Publishers, 1998.

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Cross-language information retrieval. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool, 2010.

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Peters, C. Multilingual information retrieval. Heidelberg: Springer, 2012.

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Padó, Sebastian. Cross-lingual annotation projection models for role-semantic information. Saarbrücken: Saarland University, 2007.

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Peters, Carol, ed. Cross-Language Information Retrieval and Evaluation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44645-1.

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Peters, Carol, Martin Braschler, Julio Gonzalo, and Michael Kluck, eds. Advances in Cross-Language Information Retrieval. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/b12018.

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Book chapters on the topic "Cross lingual information retrieval"

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Fluhr, Christian, Dominique Schmit, Philippe Ortet, Faza Elkateb, Karine Gurtner, and Khaled Radwan. "Distributed Cross-Lingual Information Retrieval." In Cross-Language Information Retrieval, 41–50. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5661-9_4.

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Parton, Kristen, and Jianfeng Gao. "Combining Signals for Cross-Lingual Relevance Feedback." In Information Retrieval Technology, 356–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35341-3_31.

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Lindén, Krister. "Finding Cross-Lingual Spelling Variants." In String Processing and Information Retrieval, 136–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30213-1_21.

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Jan, Ea-Ee, Shih-Hsiang Lin, and Berlin Chen. "Transliteration Retrieval Model for Cross Lingual Information Retrieval." In Information Retrieval Technology, 183–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17187-1_17.

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Rao, T. Pattabhi R. K., and Sobha Lalitha Devi. "Tamil English Cross Lingual Information Retrieval." In Multilingual Information Access in South Asian Languages, 269–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40087-2_26.

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Yang, Cheng-Zen, Che-Min Chen, and Ing-Xiang Chen. "A Cross-Lingual Framework for Web News Taxonomy Integration." In Information Retrieval Technology, 270–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11880592_21.

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Kwok, Kui-Lam, and Peter Deng. "Chinese Question-Answering: Comparing Monolingual with English-Chinese Cross-Lingual Results." In Information Retrieval Technology, 244–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11880592_19.

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Tang, Ling-Xiang, Andrew Trotman, Shlomo Geva, and Yue Xu. "Cross-Lingual Knowledge Discovery: Chinese-to-English Article Linking in Wikipedia." In Information Retrieval Technology, 286–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35341-3_24.

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Moen, Hans, and Erwin Marsi. "Cross-Lingual Random Indexing for Information Retrieval." In Statistical Language and Speech Processing, 164–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39593-2_15.

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Ballesteros, Lisa, and Bruce Croft. "Dictionary methods for cross-lingual information retrieval." In Lecture Notes in Computer Science, 791–801. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/bfb0034731.

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Conference papers on the topic "Cross lingual information retrieval"

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Pourmahmoud, Solmaz, and Mehrnoush Shamsfard. "Semantic Cross-lingual Information Retrieval." In 2008 23rd International Symposium on Computer and Information Sciences (ISCIS). IEEE, 2008. http://dx.doi.org/10.1109/iscis.2008.4717868.

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Xu, Jinxi, and Ralph Weischedel. "Cross-lingual information retrieval using hidden Markov models." In the 2000 Joint SIGDAT conference. Morristown, NJ, USA: Association for Computational Linguistics, 2000. http://dx.doi.org/10.3115/1117794.1117806.

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Nasharuddin, Nurul Amelina, Muhamad Taufik Abdullah, Rabiah Abdul Kadir, and Azreen Azman. "A review on the cross-lingual information retrieval." In Knowledge Management (CAMP). IEEE, 2010. http://dx.doi.org/10.1109/infrkm.2010.5466886.

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Xu, Jingfang, Feifei Zhai, and Zhengshan Xue. "Cross-Lingual Information Retrieve in Sogou Search." In SIGIR '17: The 40th International ACM SIGIR conference on research and development in Information Retrieval. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3077136.3096463.

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Xu, Jinxi, Ralph Weischedel, and Chanh Nguyen. "Evaluating a probabilistic model for cross-lingual information retrieval." In the 24th annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 2001. http://dx.doi.org/10.1145/383952.383968.

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Dini, Luca, Wim Peters, Doris Liebwald, Erich Schweighofer, Laurens Mommers, and Wim Voermans. "Cross-lingual legal information retrieval using a WordNet architecture." In the 10th international conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1165485.1165510.

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Litschko, Robert, Goran Glavaš, Simone Paolo Ponzetto, and Ivan Vulić. "Unsupervised Cross-Lingual Information Retrieval Using Monolingual Data Only." In SIGIR '18: The 41st International ACM SIGIR conference on research and development in Information Retrieval. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3209978.3210157.

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Virga, Paola, and Sanjeev Khudanpur. "Transliteration of proper names in cross-lingual information retrieval." In the ACL 2003 workshop. Morristown, NJ, USA: Association for Computational Linguistics, 2003. http://dx.doi.org/10.3115/1119384.1119392.

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Trieschnigg, Dolf, Djoerd Hiemstra, Franciska de Jong, and Wessel Kraaij. "A cross-lingual framework for monolingual biomedical information retrieval." In the 19th ACM international conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1871437.1871463.

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Nikesh, P. L., Sumam Mary Idicula, and S. David Peter. "English-Malayalam Cross-Lingual Information Retrieval — an experience." In 2008 IEEE International Conference on Electro/Information Technology (EIT 2008). IEEE, 2008. http://dx.doi.org/10.1109/eit.2008.4554312.

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Reports on the topic "Cross lingual information retrieval"

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Bader, Brett William, Peter Chew, Ahmed Abdelali, and Tamara Gibson Kolda. Cross-language information retrieval using PARAFAC2. Office of Scientific and Technical Information (OSTI), May 2007. http://dx.doi.org/10.2172/908061.

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Demner-Fushman, Dina, and Douglas W. Oard. The Effect of Bilingual Term List Size on Dictionary-Based Cross-Language Information Retrieval. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada447948.

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Demner-Fushman, Dina, and Douglas W. Oard. The Effect of Bilingual Term List Size on Dictionary-Based Cross-Language Information Retrieval. Fort Belvoir, VA: Defense Technical Information Center, February 2003. http://dx.doi.org/10.21236/ada452813.

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Dorr, Bonnie J., Dekang Lin, and Gina-Anne Levow. Construction of a Chinese-English Verb Lexicon for Embedded Machine Translation in Cross-Language Information Retrieval. Fort Belvoir, VA: Defense Technical Information Center, August 2002. http://dx.doi.org/10.21236/ada459245.

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You might also be interested in the extended bibliographies on the topic 'Cross lingual information retrieval' for particular source types:
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