Relevant bibliographies by topics / Text retrieval

Contents

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

Academic literature on the topic 'Text retrieval'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Text retrieval"

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Rubin, Ohad, and Jonathan Berant. "Retrieval-Pretrained Transformer: Long-range Language Modeling with Self-retrieval." Transactions of the Association for Computational Linguistics 12 (2024): 1197–213. http://dx.doi.org/10.1162/tacl_a_00693.

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Abstract Retrieval-augmented language models (LMs) have received much attention recently. However, typically the retriever is not trained jointly as a native component of the LM, but added post-hoc to an already-pretrained LM, which limits the ability of the LM and the retriever to adapt to one another. In this work, we propose the Retrieval-Pretrained Transformer (RPT), an architecture and training procedure for jointly training a retrieval-augmented LM from scratch and applying it to the task of modeling long texts. Given a recently generated text chunk in a long document, the LM computes qu
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Cui, Chenhao, and Zhoujun Li. "Prompt-Enhanced Generation for Multimodal Open Question Answering." Electronics 13, no. 8 (2024): 1434. http://dx.doi.org/10.3390/electronics13081434.

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Multimodal open question answering involves retrieving relevant information from both images and their corresponding texts given a question and then generating the answer. The quality of the generated answer heavily depends on the quality of the retrieved image–text pairs. Existing methods encode and retrieve images and texts, inputting the retrieved results into a language model to generate answers. These methods overlook the semantic alignment of image–text pairs within the information source, which affects the encoding and retrieval performance. Furthermore, these methods are highly depende
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Li, Peize, Qingyi Si, Peng Fu, Zheng Lin, and Yan Wang. "Multimodal Hypothetical Summary for Retrieval-based Multi-image Question Answering." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 5 (2025): 4851–59. https://doi.org/10.1609/aaai.v39i5.32513.

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Retrieval-based multi-image question answering (QA) task involves retrieving multiple question-related images and synthesizing these images to generate an answer. Conventional "retrieve-then-answer" pipelines often suffer from cascading errors because the training objective of QA fails to optimize the retrieval stage. To address this issue, we propose a novel method to effectively introduce and reference retrieved information into the QA. Given the image set to be retrieved, we employ a multimodal large language model (visual perspective) and a large language model (textual perspective) to obt
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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 (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
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Kim, Taewhan, Soeun Lee, Si-Woo Kim, and Dong-Jin Kim. "ViPCap: Retrieval Text-Based Visual Prompts for Lightweight Image Captioning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 4 (2025): 4320–28. https://doi.org/10.1609/aaai.v39i4.32454.

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Recent lightweight image captioning models using retrieved data mainly focus on text prompts. However, previous works only utilize the retrieved text as text prompts, and the visual information relies only on the CLIP visual embedding. Because of this issue, there is a limitation that the image descriptions inherent in the prompt are not sufficiently reflected in the visual embedding space. To tackle this issue, we propose ViPCap, a novel retrieval text-based visual prompt for lightweight image captioning. ViPCap leverages the retrieved text with image information as visual prompts to enhance
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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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Alsubhi, Kholoud, Amani Jamal, and Areej Alhothali. "Deep learning-based approach for Arabic open domain question answering." PeerJ Computer Science 8 (May 4, 2022): e952. http://dx.doi.org/10.7717/peerj-cs.952.

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Open-domain question answering (OpenQA) is one of the most challenging yet widely investigated problems in natural language processing. It aims at building a system that can answer any given question from large-scale unstructured text or structured knowledge-base. To solve this problem, researchers traditionally use information retrieval methods to retrieve the most relevant documents and then use answer extractions techniques to extract the answer or passage from the candidate documents. In recent years, deep learning techniques have shown great success in OpenQA by using dense representation
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Bin Rodzman, Shaiful Bakhtiar, Normaly Kamal Ismail, Nurazzah Abd Rahman, Syed Ahmad Aljunid, Zulhilmi Mohamed Nor, and Ahmad Yunus Mohd Noor. "Domain specific concept ontologies and text summarization as hierarchical fuzzy logic ranking indicator on malay text corpus." Indonesian Journal of Electrical Engineering and Computer Science 15, no. 3 (2019): 1527. http://dx.doi.org/10.11591/ijeecs.v15.i3.pp1527-1534.

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<span>Ranking function is a predictive algorithm that is used to establish a simple ordering of documents according to its relevance. This step is critical because the results’ quality of a Domain Specific Information Retrieval (IR) such as Hadith Information Retrieval is fundamentally dependent of the ranking function. A Hierarchical Fuzzy Logic Controller of <em>Mamdani</em>-type Fuzzy Inference System has been built to define the ranking function, based on the Malay Information retrieval’s BM25 Model. The model examines three-inputs (Ontology BM25 Score, Fabrication Rate o
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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 (2013): 35–40. http://dx.doi.org/10.5121/ijcseit.2013.3404.

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Dissertations / Theses on the topic "Text retrieval"

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Fan, Huihui. "Text Generation with and without Retrieval." Electronic Thesis or Diss., Université de Lorraine, 2021. http://www.theses.fr/2021LORR0164.

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Tous les jours, nous écrivons --- qu'il s'agisse d'envoyer un texte rapide à votre mère ou de rédiger un article scientifique tel que cette thèse. Les logiciels modernes de messagerie instantanée suggèrent souvent le mot à écrire ensuite, les courriers électroniques peuvent être lancés à l'aide d'un autocomposeur et les rédactions sont améliorées grâce à des suggestions de la machine. Ces technologies sont le fruit d'années de recherche sur la génération de texte, un domaine du traitement du langage naturel dont l'objectif est de produire automatiquement un langage naturel fluide et lisible pa
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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 show
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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 enter
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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
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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<br>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
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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
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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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Books on the topic "Text retrieval"

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

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

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

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

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

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Mauldin, Michael L. Information retrieval by text skimming. University Microfilms International, 1990.

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

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1949-, Kimberley Robert, and Institute of Information Scientists, eds. Text retrieval: A directory of software. 3rd ed. Gower, 1990.

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1955-, Hamilton Catherine D., Kimberley Robert 1949-, Smith Christine H. 1949-, and Institute of Information Scientists. Southern Branch., eds. Text retrieval: A directory of software. Gower, 1985.

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1956-, Moore Caroline, ed. European directory of text retrieval software. Gower, 1993.

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Book chapters on the topic "Text retrieval"

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Colomb, Robert M. "Text retrieval." In Information Spaces. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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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Desai, Padmashree, Vivek Kumar, and Chandan Srivastava. "Cross-Modal Retriever: Unsupervised Image Retrieval with Text and Reference Images." In 2024 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). IEEE, 2024. http://dx.doi.org/10.1109/conecct62155.2024.10677279.

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Wang, Jiamian, Pichao Wang, Guohao Sun, et al. "Text Is MASS: Modeling as Stochastic Embedding for Text-Video Retrieval." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.01566.

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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 au
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Downie, J. Stephen. "Music retrieval as text retrieval (poster abstract)." In the 22nd annual international ACM SIGIR conference. ACM Press, 1999. http://dx.doi.org/10.1145/312624.312727.

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Harman, Donna. "The Text REtrieval Conferences." In a workshop. 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. 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. Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.repl4nlp-1.20.

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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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Reports on the topic "Text retrieval"

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Harman, D. K. The fourth text REtrieval conference. 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. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada470518.

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Chew, Robert F., Kirsty J. Weitzel, Peter Baumgartner, et al. Improving Text Classification with Boolean Retrieval for Rare Categories: A Case Study Identifying Firearm Violence Conversations in the Crisis Text Line Database. RTI Press, 2023. http://dx.doi.org/10.3768/rtipress.2023.mr.0050.2304.

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Advancements in machine learning and natural language processing have made text classification increasingly attractive for information retrieval. However, developing text classifiers is challenging when no prior labeled data are available for a rare category of interest. Finding instances of the rare class using a uniform random sample can be inefficient and costly due to the rare category’s low base rate. This work presents an approach that combines the strengths of text classification and Boolean retrieval to help learn rare concepts of interest. As a motivating example, we use the task of f
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Soboroff, Ian. The Thirty-Third Text Retrieval Conference. National Institute of Standards and Technology, 2024. http://dx.doi.org/10.6028/nist.sp.1329.

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Harman, D. K. The first text REtrieval conference (TREC-1). 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). 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). 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). 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). 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). National Institute of Standards and Technology, 2000. http://dx.doi.org/10.6028/nist.sp.500-246.

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