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010   $a3-319-74054-7
024 7 $a10.1007/978-3-319-74054-6
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035   $a(DE-He213)978-3-319-74054-6
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035   $a(MiAaPQ)EBC5590768
035   $a(Au-PeEL)EBL5590768
035   $a(OCoLC)1027218888
035   $a(PPN)224638025
035   $a(EXLCZ)994100000002485455
100   $a20180228d2018      u|         0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aAutomatic Syntactic Analysis Based on Selectional Preferences /$fby Alexander Gelbukh, Hiram Calvo
205   $a1st ed. 2018.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018.
215   $a1 online resource (VIII, 165 p.) 
225 1 $aStudies in Computational Intelligence,$x1860-9503 ;$v765
311   $a3-319-74053-9 
327   $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.
330   $aThis book describes effective methods for automatically analyzing a sentence, based on the syntactic and semantic characteristics of the elements that form it. To tackle ambiguities, the authors use selectional preferences (SP), which measure how well two words fit together semantically in a sentence. Today, many disciplines require automatic text analysis based on the syntactic and semantic characteristics of language and as such several techniques for parsing sentences have been proposed. Which is better? In this book the authors begin with simple heuristics before moving on to more complex methods that identify nouns and verbs and then aggregate modifiers, and lastly discuss methods that can handle complex subordinate and relative clauses. During this process, several ambiguities arise. SP are commonly determined on the basis of the association between a pair of words. However, in many cases, SP depend on more words. For example, something (such as grass) may be edible, depending on who is eating it (a cow?). Moreover, things such as popcorn are usually eaten at the movies, and not in a restaurant. The authors deal with these phenomena from different points of view.
410  0$aStudies in Computational Intelligence,$x1860-9503 ;$v765
606   $aComputational intelligence
606   $aComputational linguistics
606   $aNatural language processing (Computer science)
606   $aArtificial intelligence
606   $aComputational Intelligence
606   $aComputational Linguistics
606   $aNatural Language Processing (NLP)
606   $aArtificial Intelligence
615  0$aComputational intelligence.
615  0$aComputational linguistics.
615  0$aNatural language processing (Computer science).
615  0$aArtificial intelligence.
615 14$aComputational Intelligence.
615 24$aComputational Linguistics.
615 24$aNatural Language Processing (NLP).
615 24$aArtificial Intelligence.
676   $a006.35
700   $aGelbukh$b Alexander$4aut$4http://id.loc.gov/vocabulary/relators/aut$01063518
702   $aCalvo$b Hiram$4aut$4http://id.loc.gov/vocabulary/relators/aut
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910299944703321
996   $aAutomatic Syntactic Analysis Based on Selectional Preferences$92533470
997   $aUNINA