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Titlebook: Explorations in Automatic Thesaurus Discovery; Gregory Grefenstette Book 1994 Springer Science+Business Media New York 1994 Thesaurus.arti

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发表于 2025-3-21 18:03:46 | 显示全部楼层 |阅读模式
书目名称Explorations in Automatic Thesaurus Discovery
编辑Gregory Grefenstette
视频video
丛书名称The Springer International Series in Engineering and Computer Science
图书封面Titlebook: Explorations in Automatic Thesaurus Discovery;  Gregory Grefenstette Book 1994 Springer Science+Business Media New York 1994 Thesaurus.arti
描述.Explorations in Automatic Thesaurus Discovery. presentsan automated method for creating a first-draft thesaurus from rawtext. It describes natural processing steps of tokenization, surfacesyntactic analysis, and syntactic attribute extraction. From theseattributes, word and term similarity is calculated and a thesaurus iscreated showing important common terms and their relation to eachother, common verb--noun pairings, common expressions, and word familymembers. .The techniques are tested on twenty different corpora ranging frombaseball newsgroups, assassination archives, medical X-ray reports,abstracts on AIDS, to encyclopedia articles on animals, even on thetext of the book itself. The corpora range from 40,000 to 6 millioncharacters of text, and results are presented for each in theAppendix. .The methods described in the book have undergone extensive evaluation.Their time and space complexity are shown to be modest. The resultsare shown to converge to a stable state as the corpus grows. Thesimilarities calculated are compared to those produced bypsychological testing. A method of evaluation using ArtificialSynonyms is tested. Gold Standards evaluation show that techniquessignif
出版日期Book 1994
关键词Thesaurus; artificial intelligence; complexity; corpus; semantic analysis
版次1
doihttps://doi.org/10.1007/978-1-4615-2710-7
isbn_softcover978-1-4613-6167-1
isbn_ebook978-1-4615-2710-7Series ISSN 0893-3405
issn_series 0893-3405
copyrightSpringer Science+Business Media New York 1994
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发表于 2025-3-21 20:49:31 | 显示全部楼层
Applications,trate how two knowledge-poor techniques can reinforce each other. Then we show how a deeper exploration of the information extracted by SEXTANT permits the creation of clusters of words along semantic axes. Finally in the last section of this chapter, we organize all the disparate techniques develop
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0893-3405 pared to those produced bypsychological testing. A method of evaluation using ArtificialSynonyms is tested. Gold Standards evaluation show that techniquessignif978-1-4613-6167-1978-1-4615-2710-7Series ISSN 0893-3405
发表于 2025-3-22 05:01:44 | 显示全部楼层
https://doi.org/10.1007/978-3-030-12771-8trate how two knowledge-poor techniques can reinforce each other. Then we show how a deeper exploration of the information extracted by SEXTANT permits the creation of clusters of words along semantic axes. Finally in the last section of this chapter, we organize all the disparate techniques develop
发表于 2025-3-22 10:19:25 | 显示全部楼层
https://doi.org/10.1007/978-3-030-95806-0ne concept in a variety of ways. This variability raises the question of how the computer can know that words a person uses are related to words found in stored text? Any computer-based system that employs a natural, rather than an artificial, language in its dialogue with human users is faced with this problem.
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Evaluation,ompare words and produce list of similar words. Visual inspection of these lists gives the intuitive impression that the words on this lists are related. The purpose of this chapter is to demonstrate in some objective manner that the relationships extracted are what are commonly considered considered as semantic relationships.
发表于 2025-3-23 08:32:44 | 显示全部楼层
Conclusion,motivation of this approach: the . problem that affects any computer-based manipulation of text, e.g., information retrieval, filtering, language understanding, human-computer interfaces, machine translation. This problem generated much research in computer-based semantics, a portion of which we reviewed before we presented our system SEXTANT.
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