Journal articles: 'Text retrieval'

1

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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Blair, David C. "Text retrieval: Information first." Journal of the American Society for Information Science 44, no. 2 (March 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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Rapp, Barbara A. "Text retrieval: An introduction." Information Processing & Management 24, no. 6 (January 1988): 713. http://dx.doi.org/10.1016/0306-4573(88)90008-8.

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

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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 (September 1, 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 of Hadith and Shia Rate of Hadith) and four-output values of Final Ranking Score which consist of three triangular membership functions. The proposed system has outperformed the BM25 original score and the Vector Space Model (VM) on 16 queries, while the BM25 original score and Vector Space Model only yield better result in 9 and 2 queries respectively on the P@10, %no measures and MAP. P@10 represent the values of Precision at Rank 10 P@10), %no measures represent the percentage of queries with no relevant documents in the top ten retrieved and MAP represents Mean Average Precision of the queries. The results show the proposed system have capability to demote negative documents and move up the relevant documents in the ranking list and its capability to recall unseen document with the application of ontology in text retrieval. For the future works, the researcher would like to apply the usage of other Malay Semantic elements and another corpus for positive ranking indicator.</span>

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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 for document retrieval and reading comprehension for answer extraction. However, despite the advancement in the English language OpenQA, other languages such as Arabic have received less attention and are often addressed using traditional methods. In this paper, we use deep learning methods for Arabic OpenQA. The model consists of document retrieval to retrieve passages relevant to a question from large-scale free text resources such as Wikipedia and an answer reader to extract the precise answer to the given question. The model implements dense passage retriever for the passage retrieval task and the AraELECTRA for the reading comprehension task. The result was compared to traditional Arabic OpenQA approaches and deep learning methods in the English OpenQA. The results show that the dense passage retriever outperforms the traditional Term Frequency-Inverse Document Frequency (TF-IDF) information retriever in terms of the top-20 passage retrieval accuracy and improves our end-to-end question answering system in two Arabic question-answering benchmark datasets.

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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 (January 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 short, accurate, and fluent extract of a longer retrieved single document (or a set of top retrieved documents). Obtained results show that using word embeddings is an excellent way to achieve higher precision rates and retrieve more accurate documents. Also, obtained summaries satisfy the retention and fidelity criteria of relevant summaries.

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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 (January 31, 2005): 11–13. http://dx.doi.org/10.1002/bult.90.

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18

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-tuning framework that turns any pretrained text-image multi-modal model into an efficient retrieval model. The framework is based on a cooperative retrieve-and-rerank approach that combines: 1) twin networks (i.e., a bi-encoder) to separately encode all items of a corpus, enabling efficient initial retrieval, and 2) a cross-encoder component for a more nuanced (i.e., smarter) ranking of the retrieved small set of items. We also propose to jointly fine- tune the two components with shared weights, yielding a more parameter-efficient model. Our experiments on a series of standard cross-modal retrieval benchmarks in monolingual, multilingual, and zero-shot setups, demonstrate improved accuracy and huge efficiency benefits over the state-of-the-art cross- encoders.1

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19

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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Harman, Donna, Chris Buckley, Jamie Callan, Susan Dumais, David Lewis, Steve Robertson, Alan Smeaton, Karen Sparck Jones, and Richard Tong. "Performance of Text Retrieval Systems." Science 268, no. 5216 (June 9, 1995): 1417–18. http://dx.doi.org/10.1126/science.268.5216.1417.c.

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

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22

POSWICKosb, R. F. "Full-Text Retrieval on Microcomputers." Literary and Linguistic Computing 4, no. 2 (April 1, 1989): 108–14. http://dx.doi.org/10.1093/llc/4.2.108.

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23

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

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24

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

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25

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

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Harman, D., C. Buckley, J. Callan, S. Dumais, D. Lewis, S. Robertson, A. Smeaton, K. S. Jones, and R. Tong. "Performance of Text Retrieval Systems." Science 268, no. 5216 (June 9, 1995): 1417–18. http://dx.doi.org/10.1126/science.268.5216.1417-b.

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Salton, G. "Performance of Text Retrieval Systems." Science 268, no. 5216 (June 9, 1995): 1418–19. http://dx.doi.org/10.1126/science.268.5216.1418.

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Singer, Murray, and Walter Kintsch. "Text Retrieval: A Theoretical Exploration." Discourse Processes 31, no. 1 (January 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 (August 30, 1991): 974–80. http://dx.doi.org/10.1126/science.253.5023.974.

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Kolaiti, Patricia. "Text and contextual information retrieval." Pragmatics. Quarterly Publication of the International Pragmatics Association (IPrA) 24, no. 1 (March 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 that seem to pose problems for purely cohesion-based approaches but are straightforwardly explained by the Relevance-Theoretic account.

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31

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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Sievert, MaryEllen C. "Full-text information retrieval: Introduction." Journal of the American Society for Information Science 47, no. 4 (April 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 (January 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 (January 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 (May 1993): 287–98. http://dx.doi.org/10.1016/0306-4573(93)90056-j.

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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 (June 8, 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 for physically challenged people like blind people where the input and the output taken/given is voice. The functionality of proposed IRS consists of three stages such as: (i) Voice to Text input, (II). Pattern Matching, and (III). Text to Voice output.In order to improve the accuracy and relevancy the proposed IRS uses an indexing method called Bag of Words (BOW). BOW is like an index-table which can be referred to store, compare and retrieve the information speedily and accurately. Index-table utilization in IRS improves the accuracy with minimized computational complexity. The proposed IRS is simulated in DOTNET software and the results are compared with the existing system results in order to evaluate the performance.

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Bartling, W. C., T. K. Schleyer, and S. Visweswaran. "Retrieval and Classification of Dental Research Articles." Advances in Dental Research 17, no. 1 (December 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 classifier, created models, based on the presence or absence of words or UMLS phrases, that distinguished dental research articles from all others. These models were applied to a test set with different inputs for each article: (1) title and abstract only, (2) MeSH terms only, or (3) both. By title and abstract only, IPS correctly classified 64% of all dental research articles present in the test set. The percentage of correctly classified dental research articles in this retrieved set was 71%. MeSH term inclusion decreased performance. Computer programs that use text input to categorize articles may aid in retrieval of a broad corpus of literature better than indexing terms or key words alone.

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Cornelius, Ian. "Text retrieval in context; Proceedings of the institute of information scientists text retrieval '84 conference." Information Processing & Management 22, no. 4 (January 1986): 364–65. http://dx.doi.org/10.1016/0306-4573(86)90042-7.

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Lee, Lin-shan, James Glass, Hung-yi Lee, and Chun-an Chan. "Spoken Content Retrieval—Beyond Cascading Speech Recognition with Text Retrieval." IEEE/ACM Transactions on Audio, Speech, and Language Processing 23, no. 9 (September 2015): 1389–420. http://dx.doi.org/10.1109/taslp.2015.2438543.

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Liu, I.-Hsin. "Supporting Word Retrieval From Memory." ITL - International Journal of Applied Linguistics 163 (January 1, 2012): 1–20. http://dx.doi.org/10.1075/itl.163.01liu.

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Abstract This study investigated the effect of two meaning-oriented communicative tasks on L2 learners’ consolidation of new vocabulary met in a reading text on a familiar topic, building on the premises underlying the Dreyfus and Tsamir (2004) ‘Recognising, Building-with, and Constructing’ (RBC) model. Students in four lower intermediate EFL classes participated in the pre-test (of vocabulary size) post-test experimental study. Some of them only read the new text before taking an immediate and a delayed word retention test (control group); others read the text and afterwards completed comprehension questions (meaning-oriented receptive task). Still others, in addition, wrote a text similar in structure and contents to the input text while using the target words (meaning-oriented productive task). The fourth student group completed all three tasks consecutively. Our results show the superiority of the guided writing task over the ‘reading + comprehension questions’ and the ‘reading only’ conditions. On a theoretical level, content familiarity is shown to be an important mediator variable in early stages of vocabulary processing and consolidation.

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41

Mann, Traci, and Lyle A. Brenner. "Improving Text Memory by Organizing Interfering Text at Retrieval." American Journal of Psychology 109, no. 4 (1996): 539. http://dx.doi.org/10.2307/1423393.

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Wai Lam, M. Ruiz, and P. Srinivasan. "Automatic text categorization and its application to text retrieval." IEEE Transactions on Knowledge and Data Engineering 11, no. 6 (1999): 865–79. http://dx.doi.org/10.1109/69.824599.

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43

Bodner, Richard C., Mark H. Chignell, Nipon Charoenkitkarn, Gene Golovchinsky, and Richard W. Kopak. "The impact of text browsing on text retrieval performance." Information Processing & Management 37, no. 3 (May 2001): 507–20. http://dx.doi.org/10.1016/s0306-4573(00)00059-5.

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Wu, Zimin, and Gwyneth Tseng. "Chinese text segmentation for text retrieval: Achievements and problems." Journal of the American Society for Information Science 44, no. 9 (October 1993): 532–42. http://dx.doi.org/10.1002/(sici)1097-4571(199310)44:9<532::aid-asi3>3.0.co;2-m.

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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 (May 29, 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 different kinds of retrieval methods using feature extraction, similarity matching etc. Text Based Image Retrieval systems are used in many hospitals, but for large databases these are inefficient. To solve this problem, CBIR systems are proposed to retrieve matching images from database using automated feature extraction method. At present, medical imaging field finds extensive growth in the generation and evaluation of various types of medical images which are high inconsistency, usually fused and the combination of various minor composition structures. For easy retrieval, need to be development of feature extraction and image classification methods. Different methods are used for different kinds of medical images. The Radiology department and Cardiology department are the largest producers of medical images and the patient abnormal images can be stored with the normal images. CBIR uses query image as input and it retrieves the images, which are similar to the query more efficiently and effectively. This paper provides a comprehensive Survey about CBIR system and its one of the major application in medical domain.

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Saito, Ryo, and Toshiaki Muramoto. "Retrieval practice effect on text comprehension." Proceedings of the Annual Convention of the Japanese Psychological Association 78 (September 10, 2014): 1AM—1–094–1AM—1–094. http://dx.doi.org/10.4992/pacjpa.78.0_1am-1-094.

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47

Nozue, Michiko. "Full-text database retrieval using paragraphs." Library and Information Science 31 (March 31, 1994): 79–93. http://dx.doi.org/10.46895/lis.31.79.

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48

Feng, Xia, Zhiyi Hu, Caihua Liu, W. H. Ip, and Huiying Chen. "Text-Image Retrieval With Salient Features." Journal of Database Management 32, no. 4 (October 2021): 1–13. http://dx.doi.org/10.4018/jdm.2021100101.

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In recent years, deep learning has achieved remarkable results in the text-image retrieval task. However, only global image features are considered, and the vital local information is ignored. This results in a failure to match the text well. Considering that object-level image features can help the matching between text and image, this article proposes a text-image retrieval method that fuses salient image feature representation. Fusion of salient features at the object level can improve the understanding of image semantics and thus improve the performance of text-image retrieval. The experimental results show that the method proposed in the paper is comparable to the latest methods, and the recall rate of some retrieval results is better than the current work.

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EDWIN, MATHEW, L. KARTHIKEYAN, and B. MUTHU SENTHIL. "KEYWORD-BASED TEXT DOCUMENT RETRIEVAL SYSTEM." i-manager's Journal on Information Technology 9, no. 4 (2020): 1. http://dx.doi.org/10.26634/jit.9.4.18244.

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Prabhakar, Dinesh Kumar, Sukomal Pal, and Chiranjeev Kumar. "Query Expansion for Tansliterated Text Retrieval." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 4 (January 7, 2021): 1–34. http://dx.doi.org/10.1145/3447649.

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With Web 2.0, there has been exponential growth in the number of Web users and the volume of Web content. Most of these users are not only consumers of the information but also generators of it. People express themselves here in colloquial languages, but using Roman script (transliteration). These texts are mostly informal and casual, and therefore seldom follow grammar rules. Also, there does not exist any prescribed set of spelling rules in transliterated text. This freedom leads to large-scale spelling variations, which is a major challenge in mixed script information processing. This article studies different existing phonetic algorithms to handle the issue of spelling variation, points out the limitations of them, and proposes a novel phonetic encoding approach with two different flavors in the light of Hindi transliteration. Experiments performed over Hindi song lyrics retrieval in mixed script domain with three different retrieval models show that proposed approaches outperform the existing techniques in a majority of the cases (sometimes statistically significantly) for a number of metrics like nDCG@1, nDCG@5, nDCG@10, MAP, MRR, and Recall.

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Journal articles on the topic 'Text retrieval'

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