Relevant bibliographies by topics / Text retrieval

Academic literature on the topic 'Text retrieval'

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Published: 4 June 2021
Last updated: 10 March 2023

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Journal articles on the topic "Text retrieval"

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Zhao Shan, 赵珊, and 汤永利 Tang Yongli. "Image Retrieval Based on Text-Retrieval Technology." Acta Optica Sinica 29, no. 10 (2009): 2721–25. http://dx.doi.org/10.3788/aos20092910.2721.

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Huang, Chunhao, Zhiyuan Zhu, and Jing Guo. "Text Retrieval Technology Based on Keyword Retrieval." Journal of Physics: Conference Series 1607 (August 2020): 012108. http://dx.doi.org/10.1088/1742-6596/1607/1/012108.

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Alikhani, Malihe, Fangda Han, Hareesh Ravi, Mubbasir Kapadia, Vladimir Pavlovic, and Matthew Stone. "Cross-Modal Coherence for Text-to-Image Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (June 28, 2022): 10427–35. http://dx.doi.org/10.1609/aaai.v36i10.21285.

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Common image-text joint understanding techniques presume that images and the associated text can universally be characterized by a single implicit model. However, co-occurring images and text can be related in qualitatively different ways, and explicitly modeling it could improve the performance of current joint understanding models. In this paper, we train a Cross-Modal Coherence Model for text-to-image retrieval task. Our analysis shows that models trained with image–text coherence relations can retrieve images originally paired with target text more often than coherence-agnostic models. We also show via human evaluation that images retrieved by the proposed coherence-aware model are preferred over a coherence-agnostic baseline by a huge margin. Our findings provide insights into the ways that different modalities communicate and the role of coherence relations in capturing commonsense inferences in text and imagery.
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Bhargava, Apeksha, and Sri Khetwat Saritha. "Information Retrieval from Text." International Journal of Computer Science, Engineering and Information Technology 3, no. 4 (August 31, 2013): 35–40. http://dx.doi.org/10.5121/ijcseit.2013.3404.

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Kissel, Rich. "STATUS: free text retrieval." Electronic Library 3, no. 3 (March 1985): 172–74. http://dx.doi.org/10.1108/eb044657.

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Visschedijk, Ankie, and Forbes Gibb. "UNCONVENTIONAL TEXT RETRIEVAL SYSTEMS." Online and CD-Rom Review 17, no. 1 (January 1993): 11–23. http://dx.doi.org/10.1108/eb024418.

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Gilchrist, Alan. "Text retrieval: an overview." Learned Publishing 16, no. 1 (January 2003): 61–69. http://dx.doi.org/10.1087/095315103320995104.

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MAYFIELD, JAMES. "Ontologies and text retrieval." Knowledge Engineering Review 17, no. 1 (March 2002): 71–75. http://dx.doi.org/10.1017/s026988890200036x.

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Analogues to much of today's work in ontologies have existed for centuries in text retrieval. The use of controlled vocabularies, or thesauri, has been fundamental to document indexing in library science. Thesauri serve several purposes, including:[bull ] Knowledge organisation A thesaurus provides a hierarchy of concepts that organises domain-specific knowledge.[bull ] Terminology normalisation By selecting a unique word or phrase to represent each domain concept, then linking synonymous terms to it, a thesaurus enforces terminological consistency.[bull ] Query expansion A thesaurus facilitates the addition of terms to a query by providing explicit hierarchical and lateral relationships among terms.These properties serve to mediate the information flow from indexer to user. Thesauri thus serve many of the same functions for people that ontologies are designed to serve for software agents. As automated retrieval has developed over the decades since the inception of computer processing of text, many techniques have been introduced to apply this typically manual work to the automated arena (see Soergel (1985) for an introduction to library information systems, also Anderson and Pélrez-Carballo (2001a, 2001b) for a summary of the intersection of human and machine indexing).
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Rasmussen, E. M. "Text retrieval: An introduction." International Journal of Information Management 9, no. 4 (December 1989): 292. http://dx.doi.org/10.1016/0268-4012(89)90055-8.

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Stevenson, Ann. "Text retrieval: Information first." International Journal of Information Management 12, no. 3 (September 1992): 248–49. http://dx.doi.org/10.1016/0268-4012(92)90012-f.

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Dissertations / Theses on the topic "Text retrieval"

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Kay, Roderick Neil. "Text analysis, summarising and retrieval." Thesis, University of Salford, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.360435.

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Lee, Hyo Sook. "Automatic text processing for Korean language free text retrieval." Thesis, University of Sheffield, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.322916.

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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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Shokouhi, Milad, and milads@microsoft com. "Federated Text Retrieval from Independent Collections." RMIT University. Computer Science and Information Technology, 2008. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080521.151632.

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Federated information retrieval is a technique for searching multiple text collections simultaneously. Queries are submitted to a subset of collections that are most likely to return relevant answers. The results returned by selected collections are integrated and merged into a single list. Federated search is preferred over centralized search alternatives in many environments. For example, commercial search engines such as Google cannot index uncrawlable hidden web collections; federated information retrieval systems can search the contents of hidden web collections without crawling. In enterprise environments, where each organization maintains an independent search engine, federated search techniques can provide parallel search over multiple collections. There are three major challenges in federated search. For each query, a subset of collections that are most likely to return relevant documents are selected. This creates the collection selection problem. To be able to select suitable collections, federated information retrieval systems acquire some knowledge about the contents of each collection, creating the collection representation problem. The results returned from the selected collections are merged before the final presentation to the user. This final step is the result merging problem. In this thesis, we propose new approaches for each of these problems. Our suggested methods, for collection representation, collection selection, and result merging, outperform state-of-the-art techniques in most cases. We also propose novel methods for estimating the number of documents in collections, and for pruning unnecessary information from collection representations sets. Although management of document duplication has been cited as one of the major problems in federated search, prior research in this area often assumes that collections are free of overlap. We investigate the effectiveness of federated search on overlapped collections, and propose new methods for maximizing the number of distinct relevant documents in the final merged results. In summary, this thesis introduces several new contributions to the field of federated information retrieval, including practical solutions to some historically unsolved problems in federated search, such as document duplication management. We test our techniques on multiple testbeds that simulate both hidden web and enterprise search environments.
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Nwesri, Abdusalam F. Ahmad, and nwesri@yahoo com. "Effective retrieval techniques for Arabic text." RMIT University. Computer Science and IT, 2008. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20081204.163422.

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Arabic is a major international language, spoken in more than 23 countries, and the lingua franca of the Islamic world. The number of Arabic-speaking Internet users has grown over nine-fold in the Middle East between the year 2000 and 2007, yet research in Arabic Information Retrieval (AIR) has not advanced as in other languages such as English. In this thesis, we explore techniques that improve the performance of AIR systems. Stemming is considered one of the most important factors to improve retrieval effectiveness of AIR systems. Most current stemmers remove affixes without checking whether the removed letters are actually affixes. We propose lexicon-based improvements to light stemming that distinguish core letters from proper Arabic affixes. We devise rules to stem most affixes and show their effects on retrieval effectiveness. Using the TREC 2001 test collection, we show that applying relevance feedback with our rules produces significantly better results than light stemming. Techniques for Arabic information retrieval have been studied in depth on clean collections of newswire dispatches. However, the effectiveness of such techniques is not known on other noisy collections in which text is generated using automatic speech recognition (ASR) systems and queries are generated using machine translations (MT). Using noisy collections, we show that normalisation, stopping and light stemming improve results as in normal text collections but that n-grams and root stemming decrease performance. Most recent AIR research has been undertaken using collections that are far smaller than the collections used for English text retrieval; consequently, the significance of some published results is debatable. Using the LDC Arabic GigaWord collection that contains more than 1 500 000 documents, we create a test collection of~90 topics with their relevance judgements. Using this test collection, we show empirically that for a large collection, root stemming is not competitive. Of the approaches we have studied, lexicon-based stemming approaches perform better than light stemming approaches alone. Arabic text commonly includes foreign words transliterated into Arabic characters. Several transliterated forms may be in common use for a single foreign word, but users rarely use more than one variant during search tasks. We test the effectiveness of lexicons, Arabic patterns, and n-grams in distinguishing foreign words from native Arabic words. We introduce rules that help filter foreign words and improve the n-gram approach used in language identification. Our combined n-grams and lexicon approach successfully identifies 80% of all foreign words with a precision of 93%. To find variants of a specific foreign word, we apply phonetic and string similarity techniques and introduce novel algorithms to normalise them in Arabic text. We modify phonetic techniques used for English to suit the Arabic language, and compare several techniques to determine their effectiveness in finding foreign word variants. We show that our algorithms significantly improve recall. We also show that expanding queries using variants identified by our Soutex4 phonetic algorithm results in a significant improvement in precision and recall. Together, the approaches described in this thesis represent an important step towards realising highly effective retrieval of Arabic text.
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De, Luca Ernesto William. "Semantic support in multilingual text retrieval." Aachen Shaker, 2008. http://d-nb.info/990194914/04.

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Viana, Hugo Henrique Amorim. "Automatic information retrieval through text-mining." Master's thesis, Faculdade de Ciências e Tecnologia, 2013. http://hdl.handle.net/10362/11308.

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The dissertation presented for obtaining the Master’s Degree in Electrical Engineering and Computer Science, at Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia
Nowadays, around a huge amount of firms in the European Union catalogued as Small and Medium Enterprises (SMEs), employ almost a great portion of the active workforce in Europe. Nonetheless, SMEs cannot afford implementing neither methods nor tools to systematically adapt innovation as a part of their business process. Innovation is the engine to be competitive in the globalized environment, especially in the current socio-economic situation. This thesis provides a platform that when integrated with ExtremeFactories(EF) project, aids SMEs to become more competitive by means of monitoring schedule functionality. In this thesis a text-mining platform that possesses the ability to schedule a gathering information through keywords is presented. In order to develop the platform, several choices concerning the implementation have been made, in the sense that one of them requires particular emphasis is the framework, Apache Lucene Core 2 by supplying an efficient text-mining tool and it is highly used for the purpose of the thesis.
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Krishnan, Sharenya. "Text-Based Information Retrieval Using Relevance Feedback." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-53603.

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Europeana, a freely accessible digital library with an idea to make Europe's cultural and scientific heritage available to the public was founded by the European Commission in 2008. The goal was to deliver a semantically enriched digital content with multilingual access to it. Even though they managed to increase the content of data they slowly faced the problem of retrieving information in an unstructured form. So to complement the Europeana portal services, ASSETS (Advanced Search Service and Enhanced Technological Solutions) was introduced with services that sought to improve the usability and accessibility of Europeana. My contribution is to study different text-based information retrieval models, their relevance feedback techniques and to implement one simple model. The thesis explains a detailed overview of the information retrieval process along with the implementation of the chosen strategy for relevance feedback that generates automatic query expansion. Finally, the thesis concludes with the analysis made using relevance feedback, discussion on the model implemented and then an assessment on future use of this model both as a continuation of my work and using this model in ASSETS.
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Westmacott, Mike. "Content based image retrieval : analogies with text." Thesis, University of Southampton, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.423038.

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Murad, Masrah Azrifah Azmi. "Fuzzy text mining for intelligent information retrieval." Thesis, University of Bristol, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.416830.

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Books on the topic "Text retrieval"

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Losee, Robert M. Text Retrieval and Filtering. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5705-0.

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Text information retrieval systems. San Diego: Academic Press, 1992.

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T, Meadow Charles, Boyce Bert R, and Kraft Donald H, eds. Text information retrieval systems. 3rd ed. Amsterdam: Academic, 2007.

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R, Boyce Bert, and Kraft Donald H, eds. Text information retrieval systems. 2nd ed. San Diego: Academic Press, 2000.

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Willett, Peter. Best-match text retrieval. London: Library Information Technology Centre, 1993.

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1959-, Rowlands Ian, ed. Text retrieval: An introduction. London: Taylor Graham, 1987.

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Bing, Jon. Conceptual text retrieval: NORIS (77). Oslo, Norway: TANO, 1988.

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Mauldin, Michael L. Information retrieval by text skimming. Ann Arbor, Mich: University Microfilms International, 1990.

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1949-, Kimberley Robert, Hamilton Catherine D. 1955-, Smith Christine H. 1949-, and Institute of Information Scientists. Southern Branch., eds. Text retrieval in context: Proceedings of the Institute of Information Scientists Text Retrieval '84 Conference. London: Taylor Graham, 1985.

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(Conference), Text retrieval '84. Text retrieval in context: Proceedings of the Institute of Information Scientists Text Retrieval'84 Conference. London: Taylor Graham, 1985.

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Book chapters on the topic "Text retrieval"

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Colomb, Robert M. "Text retrieval." In Information Spaces, 11–28. London: Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0163-5_3.

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Schäuble, Peter. "Text Retrieval." In Multimedia Information Retrieval, 49–59. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6163-7_3.

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Bugaje, Maryam, and Gobinda Chowdhury. "Data Retrieval = Text Retrieval?" In Transforming Digital Worlds, 253–62. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78105-1_29.

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Blanken, Henk, and Djoerd Hiemstra. "Searching for Text Documents." In Multimedia Retrieval, 97–124. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72895-5_4.

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Lim, Edward H. Y., James N. K. Liu, and Raymond S. T. Lee. "Text Information Retrieval." In Knowledge Seeker - Ontology Modelling for Information Search and Management, 27–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17916-7_3.

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Lee, D. L., and F. H. Lochovsky. "Text Retrieval Machines." In Topics in Information Systems, 339–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/978-3-642-82435-7_14.

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Grossman, David A., and Ophir Frieder. "Integrating Structured Data and Text." In Information Retrieval, 211–55. Dordrecht: Springer Netherlands, 2004. http://dx.doi.org/10.1007/978-1-4020-3005-5_6.

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Jones, Susan. "Information Retrieval I." In Text and Context, 35–54. London: Springer London, 1991. http://dx.doi.org/10.1007/978-1-4471-3162-5_3.

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Jones, Susan. "Information Retrieval II." In Text and Context, 55–79. London: Springer London, 1991. http://dx.doi.org/10.1007/978-1-4471-3162-5_4.

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Mauldin, Michael L. "Conceptual Understanding of Text." In Conceptual Information Retrieval, 39–54. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-4004-5_3.

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Conference papers on the topic "Text retrieval"

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Ball, Liezl, and Theo Bothma. "The capability of search tools to retrieve words with specific properties from large text collections." In ISIC: the Information Behaviour Conference. University of Borås, Borås, Sweden, 2020. http://dx.doi.org/10.47989/irisic2030.

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Introduction. With the increase in the availability of digital text collections for humanities researchers, tools to enable enhanced retrieval are required. If words with very specific properties could be retrieved from a text collection more accurate linguistic and other analyses can be made. There are a range of properties and metadata that could be specified for retrieval, from morphological data up to bibliographic data. Furthermore, the bibliographic data should not only be on item level but extended to the text-level. For example, in an anthology each section could be encoded with the author of that section. Such extended metadata will enable fine-grained retrieval. Method. In this study, current tools were evaluated to determine to what extent they allow users to retrieve words with specific properties from a text collection. Analysis. The analysis is limited to the following criteria: interface design, metadata, search options, filtering and search results. Results. Currently, it is not possible for a user to retrieve words with specific properties from a text collection. Conclusion. An extended set of metadata should be used to encode text to enable retrieval of words on a fine-grained level.
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Downie, J. Stephen. "Music retrieval as text retrieval (poster abstract)." In the 22nd annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 1999. http://dx.doi.org/10.1145/312624.312727.

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Harman, Donna. "The Text REtrieval Conferences." In a workshop. Morristown, NJ, USA: Association for Computational Linguistics, 1993. http://dx.doi.org/10.3115/1119149.1119154.

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Nie, Jian-Yun, Martin Brisebois, and Xiaobo Ren. "On Chinese text retrieval." In the 19th annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 1996. http://dx.doi.org/10.1145/243199.243270.

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Hahn, U., M. Honeck, and S. Schulz. "Subword-based text retrieval." In 36th Annual Hawaii International Conference on System Sciences, 2003. Proceedings of the. IEEE, 2003. http://dx.doi.org/10.1109/hicss.2003.1174249.

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Chiueh, Tzi-cker, Dilip N. Simha, Alankar Saxena, Saurabh Bhola, Ping-Hung Lin, and Cheng-En Pang. "Encryption Domain Text Retrieval." In 2012 IEEE 4th International Conference on Cloud Computing Technology and Science (CloudCom). IEEE, 2012. http://dx.doi.org/10.1109/cloudcom.2012.6427518.

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Song, Junshuai, Jiangshan Zhang, Jifeng Zhu, Mengyun Tang, and Yong Yang. "TRAttack”:" Text Rewriting Attack Against Text Retrieval." In Proceedings of the 7th Workshop on Representation Learning for NLP. Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.repl4nlp-1.20.

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Gao, Liying, Kai Niu, Zehong Ma, Bingliang Jiao, Tonghao Tan, and Peng Wang. "Text-Guided Visual Feature Refinement for Text-Based Person Search." In ICMR '21: International Conference on Multimedia Retrieval. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3460426.3463652.

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Gupta, Honey, Aveena Kottwani, Soniya Gogia, and Sheetal Chaudhari. "Text analysis and information retrieval of text data." In 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET). IEEE, 2016. http://dx.doi.org/10.1109/wispnet.2016.7566241.

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Harman, Donna. "The Text REtrieval Conferences (TRECs)." In a workshop. Morristown, NJ, USA: Association for Computational Linguistics, 1996. http://dx.doi.org/10.3115/1119018.1119070.

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Reports on the topic "Text retrieval"

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Harman, D. K. The fourth text REtrieval conference. Gaithersburg, MD: National Institute of Standards and Technology, 1996. http://dx.doi.org/10.6028/nist.sp.500-236.

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Schone, Patrick, Jeffrey L. Townsend, Thomas H. Crystal, and Calvin Olano. Text Retrieval via Semantic Forests. Fort Belvoir, VA: Defense Technical Information Center, November 1997. http://dx.doi.org/10.21236/ada470518.

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Harman, D. K. The first text REtrieval conference (TREC-1). Gaithersburg, MD: National Institute of Standards and Technology, 1993. http://dx.doi.org/10.6028/nist.sp.500-207.

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Harman, D. K. The second text REtrieval conference (TREC-2). Gaithersburg, MD: National Institute of Standards and Technology, 1994. http://dx.doi.org/10.6028/nist.sp.500-215.

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Voorhees, E. M., and D. K. Harman. The fifth text REtrieval conference (TREC-5). Gaithersburg, MD: National Institute of Standards and Technology, 1997. http://dx.doi.org/10.6028/nist.sp.500-238.

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Voorhees, E. M., and D. K. Harman. The sixth text Retrieval conference (TREC-6). Gaithersburg, MD: National Institute of Standards and Technology, 1998. http://dx.doi.org/10.6028/nist.sp.500-240.

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Voorhees, E. M., and D. K. Harman. The seventh text REtrieval conference (TREC-7). Gaithersburg, MD: National Institute of Standards and Technology, 1999. http://dx.doi.org/10.6028/nist.sp.500-242.

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Voorhees, E. M., and D. K. Harman. The eighth text REtrieval conference (TREC-8). Gaithersburg, MD: National Institute of Standards and Technology, 2000. http://dx.doi.org/10.6028/nist.sp.500-246.

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Voorhees, E. M., and D. K. Harman. The ninth text REtrieval conference (TREC-9). Gaithersburg, MD: National Institute of Standards and Technology, 2001. http://dx.doi.org/10.6028/nist.sp.500-249.

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Voorhees, E. M., and D. K. Harman. The tenth text REtrieval conference, TREC 2001. Gaithersburg, MD: National Institute of Standards and Technology, 2002. http://dx.doi.org/10.6028/nist.sp.500-250.

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