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001 978-3-031-02156-5
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008 220601s2015 sz | s |||| 0|eng d
020 _a9783031021565
_9978-3-031-02156-5
024 7 _a10.1007/978-3-031-02156-5
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aHarispe, Sébastien.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_978671
245 1 0 _aSemantic Similarity from Natural Language and Ontology Analysis
_h[electronic resource] /
_cby Sébastien Harispe, Sylvie Ranwez, Stefan janaqi, Jacky Montmain.
250 _a1st ed. 2015.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2015.
300 _aXV, 238 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Human Language Technologies,
_x1947-4059
505 0 _aPreface -- Acknowledgments -- Introduction to Semantic Measures -- Corpus-Based Semantic Measures -- Knowledge-Based Semantic Measures -- Methods and Datasets for the Evaluation of Semantic Measures -- Conclusion and Research Directions -- Bibliography -- Authors' Biographies .
520 _aArtificial Intelligence federates numerous scientific fields in the aim of developing machines able to assist human operators performing complex treatments---most of which demand high cognitive skills (e.g. learning or decision processes). Central to this quest is to give machines the ability to estimate the likeness or similarity between things in the way human beings estimate the similarity between stimuli. In this context, this book focuses on semantic measures: approaches designed for comparing semantic entities such as units of language, e.g. words, sentences, or concepts and instances defined into knowledge bases. The aim of these measures is to assess the similarity or relatedness of such semantic entities by taking into account their semantics, i.e. their meaning---intuitively, the words tea and coffee, which both refer to stimulating beverage, will be estimated to be more semantically similar than the words toffee (confection) and coffee, despite that the last pair has a higher syntactic similarity. The two state-of-the-art approaches for estimating and quantifying semantic similarities/relatedness of semantic entities are presented in detail: the first one relies on corpora analysis and is based on Natural Language Processing techniques and semantic models while the second is based on more or less formal, computer-readable and workable forms of knowledge such as semantic networks, thesauri or ontologies. Semantic measures are widely used today to compare units of language, concepts, instances or even resources indexed by them (e.g., documents, genes). They are central elements of a large variety of Natural Language Processing applications and knowledge-based treatments, and have therefore naturally been subject to intensive and interdisciplinary research efforts during last decades. Beyond a simple inventory and categorization of existing measures, the aim of this monograph is to convey novices as well as researchers of these domains toward a better understanding of semantic similarity estimation and more generally semantic measures. To this end, we propose an in-depth characterization of existing proposals by discussing their features, the assumptions on which they are based and empirical results regarding their performance in particular applications. By answering these questions and by providing a detailed discussion on the foundations of semantic measures, our aim is to give the reader key knowledge required to: (i) select the more relevant methods according to a particular usage context, (ii) understand the challenges offered to this field of study, (iii) distinguish room of improvements for state-of-the-art approaches and (iv) stimulate creativity toward the development of new approaches. In this aim, several definitions, theoretical and practical details, as well as concrete applications are presented.
650 0 _aArtificial intelligence.
_93407
650 0 _aNatural language processing (Computer science).
_94741
650 0 _aComputational linguistics.
_96146
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aNatural Language Processing (NLP).
_931587
650 2 4 _aComputational Linguistics.
_96146
700 1 _aRanwez, Sylvie.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_978672
700 1 _ajanaqi, Stefan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_978673
700 1 _aMontmain, Jacky.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_978674
710 2 _aSpringerLink (Online service)
_978675
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031010286
776 0 8 _iPrinted edition:
_z9783031032844
830 0 _aSynthesis Lectures on Human Language Technologies,
_x1947-4059
_978676
856 4 0 _uhttps://doi.org/10.1007/978-3-031-02156-5
912 _aZDB-2-SXSC
942 _cEBK
999 _c84632
_d84632