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Journal articles on the topic 'Text indexing'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Zbigniew, Kaleta. "Semantic Text Indexing." Computer Science 15, no. 1 (2014): 19. http://dx.doi.org/10.7494/csci.2014.15.1.19.

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2

Ferragina, Paolo, and Giovanni Manzini. "Indexing compressed text." Journal of the ACM 52, no. 4 (July 2005): 552–81. http://dx.doi.org/10.1145/1082036.1082039.

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3

Navarro, Gonzalo, and Nicola Prezza. "Universal compressed text indexing." Theoretical Computer Science 762 (March 2019): 41–50. http://dx.doi.org/10.1016/j.tcs.2018.09.007.

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4

Amir, Amihood, Gad M. Landau, and Esko Ukkonen. "Online timestamped text indexing." Information Processing Letters 82, no. 5 (June 2002): 253–59. http://dx.doi.org/10.1016/s0020-0190(01)00275-7.

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5

Jones, Kevin. "Text management and indexing." Learned Publishing 5, no. 3 (January 1, 1992): 168–69. http://dx.doi.org/10.1002/leap/50055.

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6

Maaß, Moritz G., and Johannes Nowak. "Text indexing with errors." Journal of Discrete Algorithms 5, no. 4 (December 2007): 662–81. http://dx.doi.org/10.1016/j.jda.2006.11.001.

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7

Ferragina, Paolo, and Roberto Grossi. "Improved Dynamic Text Indexing." Journal of Algorithms 31, no. 2 (May 1999): 291–319. http://dx.doi.org/10.1006/jagm.1998.0999.

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8

Bernardini, Giulia, Huiping Chen, Gabriele Fici, Grigorios Loukides, and Solon P. Pissis. "Reverse-Safe Text Indexing." ACM Journal of Experimental Algorithmics 26 (July 8, 2021): 1–26. http://dx.doi.org/10.1145/3461698.

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We introduce the notion of reverse-safe data structures. These are data structures that prevent the reconstruction of the data they encode (i.e., they cannot be easily reversed). A data structure D is called z - reverse-safe when there exist at least z datasets with the same set of answers as the ones stored by D . The main challenge is to ensure that D stores as many answers to useful queries as possible, is constructed efficiently, and has size close to the size of the original dataset it encodes. Given a text of length n and an integer z , we propose an algorithm that constructs a z -revers

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9

Golub, Koraljka. "Automatic Subject Indexing of Text." KNOWLEDGE ORGANIZATION 46, no. 2 (2019): 104–21. http://dx.doi.org/10.5771/0943-7444-2019-2-104.

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Automatic subject indexing addresses problems of scale and sustainability and can be at the same time used to enrich existing metadata records, establish more connections across and between resources from various metadata and resource collections, and enhance consistency of the metadata. In this work, automatic subject indexing focuses on assigning index terms or classes from established knowledge organization systems (KOSs) for subject indexing like thesauri, subject headings systems and classification systems. The following major approaches are discussed, in terms of their similarities and d

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10

Belazzougui, Djamal, and Gonzalo Navarro. "Alphabet-Independent Compressed Text Indexing." ACM Transactions on Algorithms 10, no. 4 (August 2014): 1–19. http://dx.doi.org/10.1145/2635816.

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11

Hon, Wing-Kai, Tsung-Han Ku, Rahul Shah, Sharma V. Thankachan, and Jeffrey Scott Vitter. "Compressed text indexing with wildcards." Journal of Discrete Algorithms 19 (March 2013): 23–29. http://dx.doi.org/10.1016/j.jda.2012.12.003.

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12

Grist, Deirdre. "Indexing legislative text: Alberta Hansard." Indexer: The International Journal of Indexing: Volume 23, Issue 3 23, no. 3 (April 1, 2003): 138–39. http://dx.doi.org/10.3828/indexer.2003.23.3.7.

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13

Lancaster, F. W. "Retrieval experiments: Full text versus human indexing versus automatic indexing." Journal of the American Society for Information Science 49, no. 5 (1998): 483–84. http://dx.doi.org/10.1002/(sici)1097-4571(19980415)49:5<483::aid-asi13>3.0.co;2-a.

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14

Lancaster, F. W. "Retrieval experiments: Full text versus human indexing versus automatic indexing." Journal of the American Society for Information Science 49, no. 5 (1998): 484. http://dx.doi.org/10.1002/(sici)1097-4571(19980415)49:5<484::aid-asi14>3.0.co;2-6.

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15

Lancaster, F. W. "Retrieval experiments: Full text versus human indexing versus automatic indexing." Journal of the American Society for Information Science 49, no. 5 (April 15, 1998): 484. http://dx.doi.org/10.1002/(sici)1097-4571(19980415)49:5<484::aid-asi14>3.3.co;2-y.

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16

Rocher, Tatiana, Mathieu Giraud, and Mikaël Salson. "Indexing labeled sequences." PeerJ Computer Science 4 (March 26, 2018): e148. http://dx.doi.org/10.7717/peerj-cs.148.

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Background Labels are a way to add some information on a text, such as functional annotations such as genes on a DNA sequences. V(D)J recombinations are DNA recombinations involving two or three short genes in lymphocytes. Sequencing this short region (500 bp or less) produces labeled sequences and brings insight in the lymphocyte repertoire for onco-hematology or immunology studies. Methods We present two indexes for a text with non-overlapping labels. They store the text in a Burrows–Wheeler transform (BWT) and a compressed label sequence in a Wavelet Tree. The label sequence is taken in the

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17

Smiraglia, Richard P. "Keywords, Indexing, Text Analysis: An Editorial." KNOWLEDGE ORGANIZATION 40, no. 3 (2013): 155–59. http://dx.doi.org/10.5771/0943-7444-2013-3-155.

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18

Salminen, Airi, Jean Tague-Sutcliffe, and Charles McClellan. "From text to hypertext by indexing." ACM Transactions on Information Systems 13, no. 1 (January 2, 1995): 69–99. http://dx.doi.org/10.1145/195705.195717.

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19

Gey, Fredric, and Daniel P. Dabney. "Full-text against intellectual indexing controversy." Journal of the American Society for Information Science 41, no. 8 (December 1990): 613–14. http://dx.doi.org/10.1002/(sici)1097-4571(199012)41:8<613::aid-asi9>3.0.co;2-d.

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20

Bille, Philip, Johannes Fischer, Inge Li Gørtz, Tsvi Kopelowitz, Benjamin Sach, and Hjalte Wedel Vildhøj. "Sparse Text Indexing in Small Space." ACM Transactions on Algorithms 12, no. 3 (June 15, 2016): 1–19. http://dx.doi.org/10.1145/2836166.

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21

ZHANG, MENG, LIANG HU, and YI ZHANG. "WEIGHTED AUTOMATA FOR FULL-TEXT INDEXING." International Journal of Foundations of Computer Science 22, no. 04 (June 2011): 921–43. http://dx.doi.org/10.1142/s0129054111008490.

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Full-text index structures are widely used in string matching and bioinformatics. These structures such as DAWGs and suffix trees allow fast searches on texts. In this paper, we present a new partition of the factors of a word, called a consistent minimal linear partition. Based on this partition, we introduce the weighted directed word graph (WDWG), a space-economical full-text index. WDWGs are basically cyclic, which means that they may accept infinite strings. But by assigning weights to edges, the acceptable strings are limited only to the factors of the input string. For a given word w, a

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22

Rao .S, Venkata. "Correlation Preserving Indexing Based Text Clustering." IOSR Journal of Computer Engineering 13, no. 1 (2013): 27–30. http://dx.doi.org/10.9790/0661-1312730.

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23

Navarro, Gonzalo, Erkki Sutinen, and Jorma Tarhio. "Indexing text with approximate q-grams." Journal of Discrete Algorithms 3, no. 2-4 (June 2005): 157–75. http://dx.doi.org/10.1016/j.jda.2004.08.003.

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24

Ferragina, Paolo. "Dynamic Text Indexing under String Updates." Journal of Algorithms 22, no. 2 (February 1997): 296–328. http://dx.doi.org/10.1006/jagm.1996.0814.

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25

Gibney, Daniel, and Sharma V. Thankachan. "Text Indexing for Regular Expression Matching." Algorithms 14, no. 5 (April 23, 2021): 133. http://dx.doi.org/10.3390/a14050133.

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Finding substrings of a text T that match a regular expression p is a fundamental problem. Despite being the subject of extensive research, no solution with a time complexity significantly better than O(|T||p|) has been found. Backurs and Indyk in FOCS 2016 established conditional lower bounds for the algorithmic problem based on the Strong Exponential Time Hypothesis that helps explain this difficulty. A natural question is whether we can improve the time complexity for matching the regular expression by preprocessing the text T? We show that conditioned on the Online Matrix–Vector Multiplica

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26

Lienhart, Rainer, and Wolfgang Effelsberg. "Automatic text segmentation and text recognition for video indexing." Multimedia Systems 8, no. 1 (January 1, 2000): 69–81. http://dx.doi.org/10.1007/s005300050006.

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27

ŽĎÁREK, JAN, and BOŘIVOJ MELICHAR. "TREE-BASED 2D INDEXING." International Journal of Foundations of Computer Science 22, no. 08 (December 2011): 1893–907. http://dx.doi.org/10.1142/s0129054111009100.

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A new approach to the 2D pattern matching and specifically to 2D text indexing is proposed. A transformation of a 2D text into the form of a tree is presented. It preserves the context of each element of the 2D text. The tree can be linearised using the prefix notation into the form of a string (a linear text) and the pattern matching is performed in this text. Pushdown automata indexing the 2D text are constructed over the tree representation. They allow to search for 2D prefixes, 2D suffixes, and 2D factors of the 2D text in time proportional to the size of the representation of a 2D pattern

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28

Ul Hassan, Mohamed Manzoor. "A Robust Multi-Keyword Text Content Retrieval by Utilizing Hash Indexing." International Journal of Innovative Research in Computer Science & Technology 9, no. 2 (March 2021): 1–5. http://dx.doi.org/10.21276/ijircst.2021.9.2.1.

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29

Chatterjee, Niladri, and Pramod Kumar Sahoo. "Random Indexing and Modified Random Indexing based approach for extractive text summarization." Computer Speech & Language 29, no. 1 (January 2015): 32–44. http://dx.doi.org/10.1016/j.csl.2014.07.001.

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30

Sutcliffe, Glyn. "The indexing of biography as a special genre or as historically documented text." Indexer: The International Journal of Indexing: Volume 39, Issue 2 39, no. 2 (June 1, 2021): 151–63. http://dx.doi.org/10.3828/indexer.2021.16.

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The indexing of biography as a genre, per se, is reconsidered with respect to subject indexing in general and the histories of lives in particular. The index of a biography of the chess player Bobby Fischer is compared with the index of a history of chess at the height of the Cold War conflict in which Bobby Fischer was the central protagonist. Some received theory of the indexing of biographies is critiqued and challenged by practical comparisons. Indexing from a literary perspective is considered and contrasted with back-of-book indexing from an information retrieval standpoint.

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31

Moreo Fernández, Alejandro, Andrea Esuli, and Fabrizio Sebastiani. "Lightweight Random Indexing for Polylingual Text Classification." Journal of Artificial Intelligence Research 57 (October 13, 2016): 151–85. http://dx.doi.org/10.1613/jair.5194.

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Multilingual Text Classification (MLTC) is a text classification task in which documents are written each in one among a set L of natural languages, and in which all documents must be classified under the same classification scheme, irrespective of language. There are two main variants of MLTC, namely Cross-Lingual Text Classification (CLTC) and Polylingual Text Classification (PLTC). In PLTC, which is the focus of this paper, we assume (differently from CLTC) that for each language in L there is a representative set of training documents; PLTC consists of improving the accuracy of each of the

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32

Boubekeur, Fatiha, and Wassila Azzoug. "Concept-Based Indexing in Text Information Retrieval." International Journal of Computer Science and Information Technology 5, no. 1 (February 28, 2013): 119–36. http://dx.doi.org/10.5121/ijcsit.2013.5110.

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33

Navarro, Gonzalo. "Indexing text using the Ziv–Lempel trie." Journal of Discrete Algorithms 2, no. 1 (March 2004): 87–114. http://dx.doi.org/10.1016/s1570-8667(03)00066-2.

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34

Byers, David. "Full-text indexing of non-textual resources." Computer Networks and ISDN Systems 30, no. 1-7 (April 1998): 141–48. http://dx.doi.org/10.1016/s0169-7552(98)00059-2.

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35

Rachidi, Youssef. "Text Detection in Video for Video Indexing." International Journal of Computer Trends and Technology 68, no. 4 (April 25, 2020): 96–99. http://dx.doi.org/10.14445/22312803/ijctt-v68i4p117.

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36

Woodruff, Allison Gyle, and Christian Plaunt. "GIPSY: Automated geographic indexing of text documents." Journal of the American Society for Information Science 45, no. 9 (October 1994): 645–55. http://dx.doi.org/10.1002/(sici)1097-4571(199410)45:9<645::aid-asi2>3.0.co;2-8.

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37

Weinberg, Bella Hass. "Challenges in indexing electronic text and images." Journal of the American Society for Information Science 45, no. 9 (October 1994): 718–23. http://dx.doi.org/10.1002/(sici)1097-4571(199410)45:9<718::aid-asi9>3.0.co;2-f.

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38

Rahim, Robbi, Nuning Kurniasih, Muhammad Dedi Irawan, Yustria Handika Siregar, Abdurrozzaq Hasibuan, Deffi Ayu Puspito Sari, Tiarma Simanihuruk, et al. "Latent Semantic Indexing for Indonesian Text Similarity." International Journal of Engineering & Technology 7, no. 2.3 (March 8, 2018): 73. http://dx.doi.org/10.14419/ijet.v7i2.3.12619.

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Document is a written letter that can be used as evidence of information. Plagiarism is a deliberate or unintentional act of obtaining or attempting to obtain credit or value for a scientific work, citing some or all of the scientific work of another party acknowledged as a scientific work without stating the source properly and adequately. Latent Semantic Indexing method serves to find text that has the same text against from a document. The algorithm used is TF/IDF Algorithm that is the result of multiplication of TF value with IDF for a term in document while Vector Space Model (VSM) is met

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39

Villarroel, Miguel, Pablo de la Fuente, Alberto Pedrero, Jesús Vegas, and Joaquín Adiego. "Obtaining feedback for indexing from highlighted text." Electronic Library 20, no. 4 (August 2002): 306–13. http://dx.doi.org/10.1108/02640470210438919.

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40

Svenonius, Elaine. "Challenges in Indexing Electronic Text and Images." Information Processing & Management 31, no. 2 (March 1995): 259–60. http://dx.doi.org/10.1016/0306-4573(95)80048-x.

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41

Arroyuelo, Diego, Gonzalo Navarro, and Kunihiko Sadakane. "Stronger Lempel-Ziv Based Compressed Text Indexing." Algorithmica 62, no. 1-2 (September 8, 2010): 54–101. http://dx.doi.org/10.1007/s00453-010-9443-8.

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42

Mansour, Nashat, Ramzi A. Haraty, Walid Daher, and Manal Houri. "An auto-indexing method for Arabic text." Information Processing & Management 44, no. 4 (July 2008): 1538–45. http://dx.doi.org/10.1016/j.ipm.2007.12.007.

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43

Esler, Sandra L., and Michael L. Nelson. "NASA indexing benchmarks: evaluating text search engines." Journal of Network and Computer Applications 20, no. 4 (October 1997): 339–53. http://dx.doi.org/10.1006/jnca.1997.0049.

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44

Dai, Suyang, Ronghui You, Zhiyong Lu, Xiaodi Huang, Hiroshi Mamitsuka, and Shanfeng Zhu. "FullMeSH: improving large-scale MeSH indexing with full text." Bioinformatics 36, no. 5 (October 9, 2019): 1533–41. http://dx.doi.org/10.1093/bioinformatics/btz756.

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Abstract Motivation With the rapidly growing biomedical literature, automatically indexing biomedical articles by Medical Subject Heading (MeSH), namely MeSH indexing, has become increasingly important for facilitating hypothesis generation and knowledge discovery. Over the past years, many large-scale MeSH indexing approaches have been proposed, such as Medical Text Indexer, MeSHLabeler, DeepMeSH and MeSHProbeNet. However, the performance of these methods is hampered by using limited information, i.e. only the title and abstract of biomedical articles. Results We propose FullMeSH, a large-sca

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45

Amir, Amihood, Ayelet Butman, and Ely Porat. "On the relationship between histogram indexing and block-mass indexing." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 372, no. 2016 (May 28, 2014): 20130132. http://dx.doi.org/10.1098/rsta.2013.0132.

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Histogram indexing , also known as jumbled pattern indexing and permutation indexing is one of the important current open problems in pattern matching. It was introduced about 6 years ago and has seen active research since. Yet, to date there is no algorithm that can preprocess a text T in time o (| T | 2 /polylog| T |) and achieve histogram indexing, even over a binary alphabet, in time independent of the text length. The pattern matching version of this problem has a simple linear-time solution. Block-mass pattern matching problem is a recently introduced problem, motivated by issues in mass

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46

Farrow, John. "All in the mind: concept analysis in indexing." Indexer: The International Journal of Indexing: Volume 19, Issue 4 19, no. 4 (October 1, 1995): 243–47. http://dx.doi.org/10.3828/indexer.1995.19.4.2.

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The indexing process consists of the comprehension of the document to be indexed, followed by the production of a set of index terms. Differences between academic indexing and back-of-book indexing are discussed. Text comprehension is a branch of human information processing, and it is argued that the model of text comprehension and production developed by van Dijk and Kintsch can form the basis for a cognitive process model of indexing. Strategies for testing such a model are suggested.

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47

Ding, Yi, and Xian Fu. "Topical Concept Based Text Clustering Method." Advanced Materials Research 532-533 (June 2012): 939–43. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.939.

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Text clustering typically involves clustering in a high dimensional space, which appears difficult with regard to virtually all practical settings. In addition, given a particular clustering result it is typically very hard to come up with a good explanation of why the text clusters have been constructed the way they are. . To solve these problems, based on topic concept clustering, this paper proposes a method for Chinese document clustering. In this paper, we introduce a novel topical document clustering method called Document Features Indexing Clustering (DFIC), which can identify topics ac

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48

Gupta, Shweta, Sunita Yadav, and Rajesh Prasad. "Document Retrieval using Efficient Indexing Techniques." International Journal of Business Analytics 3, no. 4 (October 2016): 64–82. http://dx.doi.org/10.4018/ijban.2016100104.

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Document retrieval plays a crucial role in retrieving relevant documents. Relevancy depends upon the occurrences of query keywords in a document. Several documents include a similar key terms and hence they need to be indexed. Most of the indexing techniques are either based on inverted index or full-text index. Inverted index create lists and support word-based pattern queries. While full-text index handle queries comprise of any sequence of characters rather than just words. Problems arise when text cannot be separated as words in some western languages. Also, there are difficulties in space

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49

Harish, B. S. "Text Document Classification: An Approach Based on Indexing." International Journal of Data Mining & Knowledge Management Process 2, no. 1 (January 31, 2012): 43–62. http://dx.doi.org/10.5121/ijdkp.2012.2104.

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50

Rose, Leonard. "Index Maker: Automatic indexing from word‐processor text." Electronic Library 5, no. 3 (March 1987): 140. http://dx.doi.org/10.1108/eb044744.

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