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020 _a9783319740546
_9978-3-319-74054-6
024 7 _a10.1007/978-3-319-74054-6
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aTEC009000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aGelbukh, Alexander.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_954936
245 1 0 _aAutomatic Syntactic Analysis Based on Selectional Preferences
_h[electronic resource] /
_cby Alexander Gelbukh, Hiram Calvo.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aVIII, 165 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-9503 ;
_v765
505 0 _aIntroduction -- First approach: sentence analysis using rewriting rules -- Second approach: constituent grammars -- Third approach: dependency trees -- Evaluation of the dependency parser -- Applications -- Prepositional phrase attachment disambiguation -- The unsupervised approach: grammar induction -- Multiple argument handling -- The need for full co-occurrence.
520 _aThis book describes effective methods for automatically analyzing a sentence, based on the syntactic and semantic characteristics of the elements that form it. To tackle ambiguities, the authors use selectional preferences (SP), which measure how well two words fit together semantically in a sentence. Today, many disciplines require automatic text analysis based on the syntactic and semantic characteristics of language and as such several techniques for parsing sentences have been proposed. Which is better? In this book the authors begin with simple heuristics before moving on to more complex methods that identify nouns and verbs and then aggregate modifiers, and lastly discuss methods that can handle complex subordinate and relative clauses. During this process, several ambiguities arise. SP are commonly determined on the basis of the association between a pair of words. However, in many cases, SP depend on more words. For example, something (such as grass) may be edible, depending on who is eating it (a cow?). Moreover, things such as popcorn are usually eaten at the movies, and not in a restaurant. The authors deal with these phenomena from different points of view.
650 0 _aComputational intelligence.
_97716
650 0 _aComputational linguistics.
_96146
650 0 _aNatural language processing (Computer science).
_94741
650 0 _aArtificial intelligence.
_93407
650 1 4 _aComputational Intelligence.
_97716
650 2 4 _aComputational Linguistics.
_96146
650 2 4 _aNatural Language Processing (NLP).
_931587
650 2 4 _aArtificial Intelligence.
_93407
700 1 _aCalvo, Hiram.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_954937
710 2 _aSpringerLink (Online service)
_954938
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319740539
776 0 8 _iPrinted edition:
_z9783319740553
776 0 8 _iPrinted edition:
_z9783030089085
830 0 _aStudies in Computational Intelligence,
_x1860-9503 ;
_v765
_954939
856 4 0 _uhttps://doi.org/10.1007/978-3-319-74054-6
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c79459
_d79459