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Journal articles on the topic 'Text retrieval'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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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2

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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7

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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8

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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9

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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10

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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11

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

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12

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

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13

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

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14

MAYFIELD, JAMES. "Ontologies and text retrieval." Knowledge Engineering Review 17, no. 1 (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 facilitat

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15

Rasmussen, E. M. "Text retrieval: An introduction." International Journal of Information Management 9, no. 4 (1989): 292. http://dx.doi.org/10.1016/0268-4012(89)90055-8.

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16

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

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17

Barr, Nancy E. "Text retrieval: Information first." Journal of Academic Librarianship 19, no. 1 (1993): 45. http://dx.doi.org/10.1016/0099-1333(93)90772-w.

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18

Blair, David C. "Text retrieval: Information first." Journal of the American Society for Information Science 44, no. 2 (1993): 113–15. http://dx.doi.org/10.1002/(sici)1097-4571(199303)44:2<113::aid-asi7>3.0.co;2-5.

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19

Rapp, Barbara A. "Text retrieval: An introduction." Information Processing & Management 24, no. 6 (1988): 713. http://dx.doi.org/10.1016/0306-4573(88)90008-8.

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20

Geigle, Gregor, Jonas Pfeiffer, Nils Reimers, Ivan Vulić, and Iryna Gurevych. "Retrieve Fast, Rerank Smart: Cooperative and Joint Approaches for Improved Cross-Modal Retrieval." Transactions of the Association for Computational Linguistics 10 (2022): 503–21. http://dx.doi.org/10.1162/tacl_a_00473.

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Abstract Current state-of-the-art approaches to cross- modal retrieval process text and visual input jointly, relying on Transformer-based architectures with cross-attention mechanisms that attend over all words and objects in an image. While offering unmatched retrieval performance, such models: 1) are typically pretrained from scratch and thus less scalable, 2) suffer from huge retrieval latency and inefficiency issues, which makes them impractical in realistic applications. To address these crucial gaps towards both improved and efficient cross- modal retrieval, we propose a novel fine-tuni

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21

Ben Ayed, Alaidine, Ismaïl Biskri, and Jean-Guy Meunier. "An End-to-End Efficient Lucene-Based Framework of Document/Information Retrieval." International Journal of Information Retrieval Research 12, no. 1 (2022): 1–14. http://dx.doi.org/10.4018/ijirr.289950.

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In the context of big data and the 4.0 industrial revolution era, enhancing document/information retrieval frameworks efficiency to handle the ever‐growing volume of text data in an ever more digital world is a must. This article describes a double-stage system of document/information retrieval. First, a Lucene-based document retrieval tool is implemented, and a couple of query expansion techniques using a comparable corpus (Wikipedia) and word embeddings are proposed and tested. Second, a retention-fidelity summarization protocol is performed on top of the retrieved documents to create a shor

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22

Tang, Tao, Dafeng Wei, Zhengyu Jia, et al. "BEV-TSR: Text-Scene Retrieval in BEV Space for Autonomous Driving." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 7 (2025): 7275–83. https://doi.org/10.1609/aaai.v39i7.32782.

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The rapid development of the autonomous driving industry has led to a significant accumulation of autonomous driving data. Consequently, there comes a growing demand for retrieving data to provide specialized optimization. However, directly applying previous image retrieval methods faces several challenges, such as the lack of global feature representation and inadequate text retrieval ability for complex driving scenes. To address these issues, firstly, we propose the BEV-TSR framework which leverages descriptive text as an input to retrieve corresponding scenes in the Bird’s Eye View (BEV) s

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23

Ajay Mukund, S., and K. S. Easwarakumar. "Optimizing Legal Text Summarization Through Dynamic Retrieval-Augmented Generation and Domain-Specific Adaptation." Symmetry 17, no. 5 (2025): 633. https://doi.org/10.3390/sym17050633.

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Legal text summarization presents distinct challenges due to the intricate and domain-specific nature of legal language. This paper introduces a novel framework integrating dynamic Retrieval-Augmented Generation (RAG) with domain-specific adaptation to enhance the accuracy and contextual relevance of legal document summaries. The proposed Dynamic Legal RAG system achieves a vital form of symmetry between information retrieval and content generation, ensuring that retrieved legal knowledge is both comprehensive and precise. Using the BM25 retriever with top-3 chunk selection, the system optimiz

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24

Gonzalez-Garcia, Lino, Miguel-Angel Sicilia, and Elena García-Barriocanal. "Using Vector Databases for the Selection of Related Occupations: An Empirical Evaluation Using O*NET." Big Data and Cognitive Computing 9, no. 7 (2025): 175. https://doi.org/10.3390/bdcc9070175.

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Career planning agencies and other organizations can help workers if they are able to effectively identify related occupations that are relevant to the task at hand. Occupational knowledge bases such as O*NET and ESCO represent mature attempts to categorize occupations and describe them in detail so that they can be used to search for related occupations. Vector databases offer an opportunity to find related occupations based on large pre-trained word and sentence embeddings and their associated retrieval algorithms for similarity search. This paper reports a systematic empirical evaluation of

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25

Uma, R., and B. Latha. "An efficient voice based information retrieval using bag of words based indexing." International Journal of Engineering & Technology 7, no. 3.3 (2018): 622. http://dx.doi.org/10.14419/ijet.v7i2.33.14850.

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Data mining is one of the leading and drastically growing researches nowadays. One of the main areas in data mining is Information Retrieval (IR). Information retrieval is a broad job and it is finding information without any structured nature. Infor-mation retrieval retrieves the user required information from a large collection of data. The existing approaches yet to improve the accuracy in terms of relevant accuracy. In this paper, it is motivated to provide an Information Retrieval System (IRS) where it can retrieve information with high relevancy. The proposed IRS is specially designed fo

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26

Harman, Donna. "The Text REtrieval Conferences (TRECs): Providing a Test-Bed for Information Retrieval Systems." Bulletin of the American Society for Information Science and Technology 24, no. 4 (2005): 11–13. http://dx.doi.org/10.1002/bult.90.

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27

Fulbright, Helen, and Connor Evans. "Finding full texts in bulk: a comparison of EndNote 20 versus Zotero 6 using the University of York’s subscriptions." Journal of the Medical Library Association 112, no. 3 (2024): 214–24. http://dx.doi.org/10.5195/jmla.2024.1880.

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Objective: To understand the performance of EndNote 20 and Zotero 6’s full text retrieval features. Methods: Using the University of York’s subscriptions, we tested and compared EndNote and Zotero’s full text retrieval. 1,000 records from four evidence synthesis projects were tested for the number of: full texts retrieved; available full texts retrieved; unique full texts (found by one program only); and differences in versions of full texts for the same record. We also tested the time taken and accuracy of retrieved full texts. One dataset was tested multiple times to confirm if the number of

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28

Mitra, Chancharik, Mihran Miroyan, Rishi Jain, Vedant Kumud, Gireeja Ranade, and Narges Norouzi. "RetLLM-E: Retrieval-Prompt Strategy for Question-Answering on Student Discussion Forums." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (2024): 23215–23. http://dx.doi.org/10.1609/aaai.v38i21.30368.

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This paper focuses on using Large Language Models to support teaching assistants in answering questions on large student forums such as Piazza and EdSTEM. Since student questions on these forums are often closely tied to specific aspects of the institution, instructor, and course delivery, general-purpose LLMs do not directly do well on this task. We introduce RetLLM-E, a method that combines text-retrieval and prompting approaches to enable LLMs to provide precise and high-quality answers to student questions. When presented with a student question, our system initiates a two-step process. Fi

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29

Zhu, Wenhao, Xiaoyu Zhang, Qiuhong Zhai, and Chenyun Liu. "A Hybrid Text Generation-Based Query Expansion Method for Open-Domain Question Answering." Future Internet 15, no. 5 (2023): 180. http://dx.doi.org/10.3390/fi15050180.

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In the two-stage open-domain question answering (OpenQA) systems, the retriever identifies a subset of relevant passages, which the reader then uses to extract or generate answers. However, the performance of OpenQA systems is often hindered by issues such as short and semantically ambiguous queries, making it challenging for the retriever to find relevant passages quickly. This paper introduces Hybrid Text Generation-Based Query Expansion (HTGQE), an effective method to improve retrieval efficiency. HTGQE combines large language models with Pseudo-Relevance Feedback techniques to enhance the

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30

Bartling, W. C., T. K. Schleyer, and S. Visweswaran. "Retrieval and Classification of Dental Research Articles." Advances in Dental Research 17, no. 1 (2003): 115–20. http://dx.doi.org/10.1177/154407370301700126.

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Successful retrieval of a corpus of literature on a broad topic can be difficult. This study demonstrates a method to retrieve the dental and craniofacial research literature. We explored MeSH manually for dental or craniofacial indexing terms. MEDLINE was searched using these terms, and a random sample of references was extracted from the resulting set. Sixteen dental research experts categorized these articles, reading only the title and abstract, as either: (1) dental research, (2) dental non-research, (3) non-dental, or (4) not sure. Identify Patient Sets (IPS), a probabilistic text classi

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31

Kim, Yangwoo. "Optoelectronic full-text retrieval system." Optical Engineering 31, no. 5 (1992): 906. http://dx.doi.org/10.1117/12.56167.

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32

Harman, Donna, Chris Buckley, Jamie Callan, et al. "Performance of Text Retrieval Systems." Science 268, no. 5216 (1995): 1417–18. http://dx.doi.org/10.1126/science.268.5216.1417.c.

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33

Voorhees, Ellen M., and Donna Harman. "The text REtrieval conference (TREC)." ACM SIGIR Forum 33, no. 2 (1999): 12–15. http://dx.doi.org/10.1145/344250.344252.

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34

POSWICKosb, R. F. "Full-Text Retrieval on Microcomputers." Literary and Linguistic Computing 4, no. 2 (1989): 108–14. http://dx.doi.org/10.1093/llc/4.2.108.

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35

Riloff, Ellen, and Lee Hollaar. "Text databases and information retrieval." ACM Computing Surveys 28, no. 1 (1996): 133–35. http://dx.doi.org/10.1145/234313.234371.

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36

Tenopir, Carol. "Full text database retrieval performance." Online Review 9, no. 2 (1985): 149–64. http://dx.doi.org/10.1108/eb024180.

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37

Lundeen, Gerald. "Textbank: Text searching and retrieval." Online Review 12, no. 1 (1988): 63–65. http://dx.doi.org/10.1108/eb024267.

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Harman, D., C. Buckley, J. Callan, et al. "Performance of Text Retrieval Systems." Science 268, no. 5216 (1995): 1417–18. http://dx.doi.org/10.1126/science.268.5216.1417-b.

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39

Salton, G. "Performance of Text Retrieval Systems." Science 268, no. 5216 (1995): 1418–19. http://dx.doi.org/10.1126/science.268.5216.1418.

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40

Singer, Murray, and Walter Kintsch. "Text Retrieval: A Theoretical Exploration." Discourse Processes 31, no. 1 (2001): 27–59. http://dx.doi.org/10.1207/s15326950dp3101_2.

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SALTON, G. "Developments in Automatic Text Retrieval." Science 253, no. 5023 (1991): 974–80. http://dx.doi.org/10.1126/science.253.5023.974.

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42

Kolaiti, Patricia. "Text and contextual information retrieval." Pragmatics. Quarterly Publication of the International Pragmatics Association (IPrA) 24, no. 1 (2014): 63–81. http://dx.doi.org/10.1075/prag.24.1.03kol.

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This paper argues for a pragmatically based reconsideration of cohesion-based approaches to information retrieval during comprehension, suggesting that a Relevance-based approach is preferable on both descriptive and explanatory grounds. It outlines a number of descriptive and explanatory problems dating back to Halliday and Hasan’s (1976, 1985; Hasan 1984) early view of cohesion, which seem to call for pragmatic solutions, and argues that interpretively used and echoic utterances raise serious questions as to the text-constitutive potential of cohesion. It goes on to discuss a number of cases

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43

Pincus, Kathy. "Shopping for text retrieval tools." Competitive Intelligence Review 3, no. 1 (1992): 40–42. http://dx.doi.org/10.1002/cir.3880030117.

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44

Sievert, MaryEllen C. "Full-text information retrieval: Introduction." Journal of the American Society for Information Science 47, no. 4 (1996): 261–62. http://dx.doi.org/10.1002/(sici)1097-4571(199604)47:4<261::aid-asi1>3.0.co;2-v.

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Cove, J. F., and B. C. Walsh. "Online text retrieval via browsing." Information Processing & Management 24, no. 1 (1988): 31–37. http://dx.doi.org/10.1016/0306-4573(88)90075-1.

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Megill, Kenneth A. "Text information and retrieval systems." Information Processing & Management 29, no. 1 (1993): 146. http://dx.doi.org/10.1016/0306-4573(93)90030-h.

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Dunlop, M. D., and C. J. van Rijsbergen. "Hypermedia and free text retrieval." Information Processing & Management 29, no. 3 (1993): 287–98. http://dx.doi.org/10.1016/0306-4573(93)90056-j.

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Park, Eunhwan, Sung-Min Lee, Dearyong Seo, Seonhoon Kim, Inho Kang, and Seung-Hoon Na. "RINK: Reader-Inherited Evidence Reranker for Table-and-Text Open Domain Question Answering." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (2023): 13446–56. http://dx.doi.org/10.1609/aaai.v37i11.26577.

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Most approaches used in open-domain question answering on hybrid data that comprises both tabular-and-textual contents are based on a Retrieval-Reader pipeline in which the retrieval module finds relevant “heterogenous” evidence for a given question and the reader module generates an answer from the retrieved evidence. In this paper, we present a Retriever-Reranker-Reader framework by newly proposing a Reader-INherited evidence reranKer (RINK) where a reranker module is designed by finetuning the reader’s neural architecture based on a simple prompting method. Our underlying assumption of reus

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49

Pokhrel, Sangita, Bina K C, and Prashant Bikram Shah. "A Practical Application of Retrieval-Augmented Generation for Website-Based Chatbots: Combining Web Scraping, Vectorization, and Semantic Search." Journal of Trends in Computer Science and Smart Technology 6, no. 4 (2025): 424–42. https://doi.org/10.36548/jtcsst.2024.4.007.

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The Retrieval-Augmented Generation (RAG) model significantly enhances the capabilities of large language models (LLMs) by integrating information retrieval with text generation, which is particularly relevant for applications requiring context-aware responses based on dynamic data sources. This research study presents a practical implementation of a RAG model personalized for a Chabot that answers user inquiries from various specific websites. The methodology encompasses several key steps: web scraping using BeautifulSoup to extract relevant content, text processing to segment this content int

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Srinivasa Reddy, K., R. Anandan, K. Kalaivani, and P. Swaminathan. "A comprehensive survey on content based image retrieval system and its application in medical domain." International Journal of Engineering & Technology 7, no. 2.31 (2018): 181. http://dx.doi.org/10.14419/ijet.v7i2.31.13436.

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Content Based Image Retrieval (CBIR) is an important and widely used technique for retrieval of different kinds of images from large database. Collection of information in database are available in different formats such as text, image, graph, chart etc. Here, our focus is on information which is available in the form of images. Searching and retrieval of the image from a large amount of database is difficult problem because it uses the image visual information such as shape, text and color for indexing and representation of an image. For efficient CBIR system, there is a need to develop diffe

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