Relevant bibliographies by topics / Cross-language information retrieval

Academic literature on the topic 'Cross-language information retrieval'

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Published: 4 June 2021
Last updated: 28 July 2024

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

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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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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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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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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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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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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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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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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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., 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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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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Dissertations / Theses on the topic "Cross-language information retrieval"

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Abusalah, Mustafa A. "Cross language information retrieval using ontologies." Thesis, University of Sunderland, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.505050.

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The basic idea behind a Cross Language Information Retrieval (CLIR) system is to retrieve documents in a language different from the query. Therefore translation is needed before matching of query and document terms can take place. This translation process tends to cause a reduction in the retrieval effectiveness of CUR as compared to monolingual Information Retrieval systems. The research introduces a new CUR approach, by producing a unique CUR system based on multilingual Arabic/English ontologies; the ontology is used for query expansion and translation. Both Arabic and English ontologies are mapped using unique automatic ontology mapping tools that will be introduced in this study as well. This research addresses lexical ambiguity problems caused by erroneous translations. To prevent this, the study proposed developing a CUR system based on a multilingual ontology to create a mapping that will solve the lexical ambiguity problem. Also this study uses ontology semantic relations to expand the query to produce a better formulated query and gain better results. Finally a weighting algorithm is applied to the result set ofthe proposed system and results are compared to a state ofthe art baseline CUR system that uses a dictionary as a translation base. The CUR system was implemented in the travel domain and two ontologies were developed. A unique ontology mapping tool was also developed to map the two ontologies. The experimental work described consists of the design, development, and evaluation of the proposed CUR system. The evaluation of the proposed system demonstrates that the retrieval effectiveness outperformed the baseline system after running two human centered experiments. Relevancy judgments were measured and the results produced indicated that the proposed system is more effective than the baseline system.
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Wang, Jianqiang. "Matching meaning for cross-language information retrieval." College Park, Md. : University of Maryland, 2005. http://hdl.handle.net/1903/3212.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2005.
Thesis research directed by: Library & Information Services. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Nic, Gearailt Donnla Brighid. "Dictionary characteristics in cross-language information retrieval." Thesis, University of Cambridge, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.619885.

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Nyman, Marie, and Maria Patja. "Cross-language information retrieval : sökfrågestruktur & sökfrågeexpansion." Thesis, Högskolan i Borås, Institutionen Biblioteks- och informationsvetenskap / Bibliotekshögskolan, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-18892.

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This Master’s thesis examines different retrieval strategies used in cross-language information retrieval (CLIR). The aim was to investigate if there were any differences between baseline queries and translated queries in retrieval effectiveness; how the retrieval effectiveness was affected by query structuring and if the results differed between different languages. The languages used in this study were Swedish, English and Finnish. 30 topics from the TrecUta collection were translated into Swedish and Finnish. Baseline queries in Swedish and Finnish were made and translated into English using a dictionary and thereby simulating automatic translation. The queries were expanded by adding all the translations from the main entries to the queries. Two kinds of queries – structured and unstructured – were designed. The queries were fed into the InQuery IR system which presented a list of retrieved documents where the relevant ones were marked. The performance of the queries was analysed by Query Performance Analyser (QPA). Average precision at seen relevant documents at DCV 10, average precision at DCV 10 and precision and recall at DCV 200 were used to measure the retrieval effectiveness. Despite the morphological differences between Swedish and Finnish, none or very small differences in retrieval performance were found, except when average precision at DCV 10 was used. The baseline queries performed the best results and the structured queries performed better in both Swedish and Finnish than the unstructured queries. The results are consistent with previous research.
Uppsatsnivå: D
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Adriani, Mirna. "A query ambiguity model for cross-language information retrieval." Thesis, University of Glasgow, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.407678.

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Loza, Christian. "Cross Language Information Retrieval for Languages with Scarce Resources." Thesis, University of North Texas, 2009. https://digital.library.unt.edu/ark:/67531/metadc12157/.

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Our generation has experienced one of the most dramatic changes in how society communicates. Today, we have online information on almost any imaginable topic. However, most of this information is available in only a few dozen languages. In this thesis, I explore the use of parallel texts to enable cross-language information retrieval (CLIR) for languages with scarce resources. To build the parallel text I use the Bible. I evaluate different variables and their impact on the resulting CLIR system, specifically: (1) the CLIR results when using different amounts of parallel text; (2) the role of paraphrasing on the quality of the CLIR output; (3) the impact on accuracy when translating the query versus translating the collection of documents; and finally (4) how the results are affected by the use of different dialects. The results show that all these variables have a direct impact on the quality of the CLIR system.
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Loza, Christian E. Mihalcea Rada F. "Cross language information retrieval for languages with scarce resources." [Denton, Tex.] : University of North Texas, 2009. http://digital.library.unt.edu/ark:/67531/metadc12157.

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Lu, Chengye. "Peer to peer English/Chinese cross-language information retrieval." Thesis, Queensland University of Technology, 2008. https://eprints.qut.edu.au/26444/1/Chengye_Lu_Thesis.pdf.

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Peer to peer systems have been widely used in the internet. However, most of the peer to peer information systems are still missing some of the important features, for example cross-language IR (Information Retrieval) and collection selection / fusion features. Cross-language IR is the state-of-art research area in IR research community. It has not been used in any real world IR systems yet. Cross-language IR has the ability to issue a query in one language and receive documents in other languages. In typical peer to peer environment, users are from multiple countries. Their collections are definitely in multiple languages. Cross-language IR can help users to find documents more easily. E.g. many Chinese researchers will search research papers in both Chinese and English. With Cross-language IR, they can do one query in Chinese and get documents in two languages. The Out Of Vocabulary (OOV) problem is one of the key research areas in crosslanguage information retrieval. In recent years, web mining was shown to be one of the effective approaches to solving this problem. However, how to extract Multiword Lexical Units (MLUs) from the web content and how to select the correct translations from the extracted candidate MLUs are still two difficult problems in web mining based automated translation approaches. Discovering resource descriptions and merging results obtained from remote search engines are two key issues in distributed information retrieval studies. In uncooperative environments, query-based sampling and normalized-score based merging strategies are well-known approaches to solve such problems. However, such approaches only consider the content of the remote database but do not consider the retrieval performance of the remote search engine. This thesis presents research on building a peer to peer IR system with crosslanguage IR and advance collection profiling technique for fusion features. Particularly, this thesis first presents a new Chinese term measurement and new Chinese MLU extraction process that works well on small corpora. An approach to selection of MLUs in a more accurate manner is also presented. After that, this thesis proposes a collection profiling strategy which can discover not only collection content but also retrieval performance of the remote search engine. Based on collection profiling, a web-based query classification method and two collection fusion approaches are developed and presented in this thesis. Our experiments show that the proposed strategies are effective in merging results in uncooperative peer to peer environments. Here, an uncooperative environment is defined as each peer in the system is autonomous. Peer like to share documents but they do not share collection statistics. This environment is a typical peer to peer IR environment. Finally, all those approaches are grouped together to build up a secure peer to peer multilingual IR system that cooperates through X.509 and email system.
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Lu, Chengye. "Peer to peer English/Chinese cross-language information retrieval." Queensland University of Technology, 2008. http://eprints.qut.edu.au/26444/.

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Peer to peer systems have been widely used in the internet. However, most of the peer to peer information systems are still missing some of the important features, for example cross-language IR (Information Retrieval) and collection selection / fusion features. Cross-language IR is the state-of-art research area in IR research community. It has not been used in any real world IR systems yet. Cross-language IR has the ability to issue a query in one language and receive documents in other languages. In typical peer to peer environment, users are from multiple countries. Their collections are definitely in multiple languages. Cross-language IR can help users to find documents more easily. E.g. many Chinese researchers will search research papers in both Chinese and English. With Cross-language IR, they can do one query in Chinese and get documents in two languages. The Out Of Vocabulary (OOV) problem is one of the key research areas in crosslanguage information retrieval. In recent years, web mining was shown to be one of the effective approaches to solving this problem. However, how to extract Multiword Lexical Units (MLUs) from the web content and how to select the correct translations from the extracted candidate MLUs are still two difficult problems in web mining based automated translation approaches. Discovering resource descriptions and merging results obtained from remote search engines are two key issues in distributed information retrieval studies. In uncooperative environments, query-based sampling and normalized-score based merging strategies are well-known approaches to solve such problems. However, such approaches only consider the content of the remote database but do not consider the retrieval performance of the remote search engine. This thesis presents research on building a peer to peer IR system with crosslanguage IR and advance collection profiling technique for fusion features. Particularly, this thesis first presents a new Chinese term measurement and new Chinese MLU extraction process that works well on small corpora. An approach to selection of MLUs in a more accurate manner is also presented. After that, this thesis proposes a collection profiling strategy which can discover not only collection content but also retrieval performance of the remote search engine. Based on collection profiling, a web-based query classification method and two collection fusion approaches are developed and presented in this thesis. Our experiments show that the proposed strategies are effective in merging results in uncooperative peer to peer environments. Here, an uncooperative environment is defined as each peer in the system is autonomous. Peer like to share documents but they do not share collection statistics. This environment is a typical peer to peer IR environment. Finally, all those approaches are grouped together to build up a secure peer to peer multilingual IR system that cooperates through X.509 and email system.
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Gupta, Parth Alokkumar. "Cross-view Embeddings for Information Retrieval." Doctoral thesis, Universitat Politècnica de València, 2017. http://hdl.handle.net/10251/78457.

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In this dissertation, we deal with the cross-view tasks related to information retrieval using embedding methods. We study existing methodologies and propose new methods to overcome their limitations. We formally introduce the concept of mixed-script IR, which deals with the challenges faced by an IR system when a language is written in different scripts because of various technological and sociological factors. Mixed-script terms are represented by a small and finite feature space comprised of character n-grams. We propose the cross-view autoencoder (CAE) to model such terms in an abstract space and CAE provides the state-of-the-art performance. We study a wide variety of models for cross-language information retrieval (CLIR) and propose a model based on compositional neural networks (XCNN) which overcomes the limitations of the existing methods and achieves the best results for many CLIR tasks such as ad-hoc retrieval, parallel sentence retrieval and cross-language plagiarism detection. We empirically test the proposed models for these tasks on publicly available datasets and present the results with analyses. In this dissertation, we also explore an effective method to incorporate contextual similarity for lexical selection in machine translation. Concretely, we investigate a feature based on context available in source sentence calculated using deep autoencoders. The proposed feature exhibits statistically significant improvements over the strong baselines for English-to-Spanish and English-to-Hindi translation tasks. Finally, we explore the the methods to evaluate the quality of autoencoder generated representations of text data and analyse its architectural properties. For this, we propose two metrics based on reconstruction capabilities of the autoencoders: structure preservation index (SPI) and similarity accumulation index (SAI). We also introduce a concept of critical bottleneck dimensionality (CBD) below which the structural information is lost and present analyses linking CBD and language perplexity.
En esta disertación estudiamos problemas de vistas-múltiples relacionados con la recuperación de información utilizando técnicas de representación en espacios de baja dimensionalidad. Estudiamos las técnicas existentes y proponemos nuevas técnicas para solventar algunas de las limitaciones existentes. Presentamos formalmente el concepto de recuperación de información con escritura mixta, el cual trata las dificultades de los sistemas de recuperación de información cuando los textos contienen escrituras en distintos alfabetos debido a razones tecnológicas y socioculturales. Las palabras en escritura mixta son representadas en un espacio de características finito y reducido, compuesto por n-gramas de caracteres. Proponemos los auto-codificadores de vistas-múltiples (CAE, por sus siglas en inglés) para modelar dichas palabras en un espacio abstracto, y esta técnica produce resultados de vanguardia. En este sentido, estudiamos varios modelos para la recuperación de información entre lenguas diferentes (CLIR, por sus siglas en inglés) y proponemos un modelo basado en redes neuronales composicionales (XCNN, por sus siglas en inglés), el cual supera las limitaciones de los métodos existentes. El método de XCNN propuesto produce mejores resultados en diferentes tareas de CLIR tales como la recuperación de información ad-hoc, la identificación de oraciones equivalentes en lenguas distintas y la detección de plagio entre lenguas diferentes. Para tal efecto, realizamos pruebas experimentales para dichas tareas sobre conjuntos de datos disponibles públicamente, presentando los resultados y análisis correspondientes. En esta disertación, también exploramos un método eficiente para utilizar similitud semántica de contextos en el proceso de selección léxica en traducción automática. Específicamente, proponemos características extraídas de los contextos disponibles en las oraciones fuentes mediante el uso de auto-codificadores. El uso de las características propuestas demuestra mejoras estadísticamente significativas sobre sistemas de traducción robustos para las tareas de traducción entre inglés y español, e inglés e hindú. Finalmente, exploramos métodos para evaluar la calidad de las representaciones de datos de texto generadas por los auto-codificadores, a la vez que analizamos las propiedades de sus arquitecturas. Como resultado, proponemos dos nuevas métricas para cuantificar la calidad de las reconstrucciones generadas por los auto-codificadores: el índice de preservación de estructura (SPI, por sus siglas en inglés) y el índice de acumulación de similitud (SAI, por sus siglas en inglés). También presentamos el concepto de dimensión crítica de cuello de botella (CBD, por sus siglas en inglés), por debajo de la cual la información estructural se deteriora. Mostramos que, interesantemente, la CBD está relacionada con la perplejidad de la lengua.
En aquesta dissertació estudiem els problemes de vistes-múltiples relacionats amb la recuperació d'informació utilitzant tècniques de representació en espais de baixa dimensionalitat. Estudiem les tècniques existents i en proposem unes de noves per solucionar algunes de les limitacions existents. Presentem formalment el concepte de recuperació d'informació amb escriptura mixta, el qual tracta les dificultats dels sistemes de recuperació d'informació quan els textos contenen escriptures en diferents alfabets per motius tecnològics i socioculturals. Les paraules en escriptura mixta són representades en un espai de característiques finit i reduït, composat per n-grames de caràcters. Proposem els auto-codificadors de vistes-múltiples (CAE, per les seves sigles en anglès) per modelar aquestes paraules en un espai abstracte, i aquesta tècnica produeix resultats d'avantguarda. En aquest sentit, estudiem diversos models per a la recuperació d'informació entre llengües diferents (CLIR , per les sevas sigles en anglès) i proposem un model basat en xarxes neuronals composicionals (XCNN, per les sevas sigles en anglès), el qual supera les limitacions dels mètodes existents. El mètode de XCNN proposat produeix millors resultats en diferents tasques de CLIR com ara la recuperació d'informació ad-hoc, la identificació d'oracions equivalents en llengües diferents, i la detecció de plagi entre llengües diferents. Per a tal efecte, realitzem proves experimentals per aquestes tasques sobre conjunts de dades disponibles públicament, presentant els resultats i anàlisis corresponents. En aquesta dissertació, també explorem un mètode eficient per utilitzar similitud semàntica de contextos en el procés de selecció lèxica en traducció automàtica. Específicament, proposem característiques extretes dels contextos disponibles a les oracions fonts mitjançant l'ús d'auto-codificadors. L'ús de les característiques proposades demostra millores estadísticament significatives sobre sistemes de traducció robustos per a les tasques de traducció entre anglès i espanyol, i anglès i hindú. Finalment, explorem mètodes per avaluar la qualitat de les representacions de dades de text generades pels auto-codificadors, alhora que analitzem les propietats de les seves arquitectures. Com a resultat, proposem dues noves mètriques per quantificar la qualitat de les reconstruccions generades pels auto-codificadors: l'índex de preservació d'estructura (SCI, per les seves sigles en anglès) i l'índex d'acumulació de similitud (SAI, per les seves sigles en anglès). També presentem el concepte de dimensió crítica de coll d'ampolla (CBD, per les seves sigles en anglès), per sota de la qual la informació estructural es deteriora. Mostrem que, de manera interessant, la CBD està relacionada amb la perplexitat de la llengua.
Gupta, PA. (2017). Cross-view Embeddings for Information Retrieval [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/78457
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Books on the topic "Cross-language information retrieval"

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

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

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Peters, Carol, Martin Braschler, Julio Gonzalo, and Michael Kluck, eds. Evaluation of Cross-Language Information Retrieval Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45691-0.

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C, Peters, ed. Advances in cross-language information retrieval: Third workshop of the Cross-Language Evaluation Forum, CLEF 2002 : revised papers. Berlin: Springer, 2003.

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C, Peters, ed. Cross-language information retrieval and evaluation: Workshop of the Cross-Language Evaluation Forum, CLEF 2000, Lisbon, Portugal, September 21-22, 2000 : revised papers. Berlin: Springer, 2001.

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C, Peters, ed. Evaluation of cross-language information retrieval systems: Second Workshop of the Cross-Language Evaluation Forum, CLEF 2001, Darmstadt, Germany, September 3-4, 2001 : revised papers. Berlin: Springer, 2002.

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Book chapters on the topic "Cross-language information retrieval"

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Grossman, David A., and Ophir Frieder. "Cross-Language Information Retrieval." In Information Retrieval, 149–79. Dordrecht: Springer Netherlands, 2004. http://dx.doi.org/10.1007/978-1-4020-3005-5_4.

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Peters, Carol, Martin Braschler, and Paul Clough. "Cross-Language Information Retrieval." In Multilingual Information Retrieval, 57–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23008-0_3.

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Shultz, Thomas R., Scott E. Fahlman, Susan Craw, Periklis Andritsos, Panayiotis Tsaparas, Ricardo Silva, Chris Drummond, et al. "Cross-Language Information Retrieval." In Encyclopedia of Machine Learning, 242. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_187.

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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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Kiyoshi, Yamabana, Muraki Kazunori, Doi Shinichi, and Kamei Shin-ichiro. "A Language Conversion Front-End for Cross-Language Information Retrieval." In Cross-Language Information Retrieval, 93–104. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5661-9_8.

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Grefenstette, Gregory. "The Problem of Cross-Language Information Retrieval." In Cross-Language Information Retrieval, 1–9. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5661-9_1.

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Ballesteros, Lisa, and W. Bruce Croft. "Statistical Methods for Cross-Language Information Retrieval." In Cross-Language Information Retrieval, 23–40. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5661-9_3.

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Hull, David. "A Weighted Boolean Model for Cross-Language Text Retrieval." In Cross-Language Information Retrieval, 119–36. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5661-9_10.

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Sheridan, Páraic, Jean Paul Ballerini, and Peter Schäuble. "Building a Large Multilingual Test Collection from Comparable News Documents." In Cross-Language Information Retrieval, 137–50. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5661-9_11.

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Oard, Douglas W., and Bonnie J. Dorr. "Evaluating Cross-Language Text Filtering Effectiveness." In Cross-Language Information Retrieval, 151–61. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5661-9_12.

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Conference papers on the topic "Cross-language information retrieval"

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Trieschnigg, Dolf. "Biomedical cross-language information retrieval." In the 31st annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1390334.1390572.

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Yang, Zhidan, and Zhiting Yang. "Cross Language Information Retrieval System." In 2nd International Forum on Management, Education and Information Technology Application (IFMEITA 2017). Paris, France: Atlantis Press, 2018. http://dx.doi.org/10.2991/ifmeita-17.2018.63.

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Flank, Sharon. "Cross-language multimedia information retrieval." In the sixth conference. Morristown, NJ, USA: Association for Computational Linguistics, 2000. http://dx.doi.org/10.3115/974147.974150.

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Hasan, Md Maruf, and Yuji Matsumoto. "Chinese-Japanese cross language information retrieval." In the ACL-2000 workshop. Morristown, NJ, USA: Association for Computational Linguistics, 2000. http://dx.doi.org/10.3115/1117724.1117727.

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Hasan, Md Maruf, and Yuji Matsumoto. "Chinese-Japanese cross language information retrieval." In the ACL-2000 Workshop. Morristown, NJ, USA: Association for Computational Linguistics, 2000. http://dx.doi.org/10.3115/1628308.1628311.

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Chew, Peter A., Brett W. Bader, Tamara G. Kolda, and Ahmed Abdelali. "Cross-language information retrieval using PARAFAC2." In the 13th ACM SIGKDD international conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1281192.1281211.

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Liu, Pengyuan, Zhijun Zheng, and Qi Su. "Cross-Language Information Retrieval Based on Multiple Information." In 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI). IEEE, 2018. http://dx.doi.org/10.1109/wi.2018.00-26.

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Ujjwal, Dasu, Prakhar Rastogi, and Siril Siddhartha. "Analysis of retrieval models for cross language information retrieval." In 2016 10th International Conference on Intelligent Systems and Control (ISCO). IEEE, 2016. http://dx.doi.org/10.1109/isco.2016.7727028.

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Chowdhury, Abdur. "Session details: Information retrieval session 3: cross language retrieval." In CIKM03: 12th International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2003. http://dx.doi.org/10.1145/3244149.

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Sperer, Ruth, and Douglas W. Oard. "Structured translation for cross-language information retrieval." In the 23rd annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 2000. http://dx.doi.org/10.1145/345508.345562.

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Reports on the topic "Cross-language 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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