Overview
- Describes recent methods for automatically analyzing a sentence, based on the syntactic and semantic characteristics of the elements that form it
- Presents a disambiguation algorithm based on linguistic and semantic knowledge
- Offers new contributions for automatic selectional preferences extraction and its multiple applications
Part of the book series: Studies in Computational Intelligence (SCI, volume 765)
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Table of contents (10 chapters)
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Front Matter
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Back Matter
Keywords
About this book
This 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.
Authors and Affiliations
Bibliographic Information
Book Title: Automatic Syntactic Analysis Based on Selectional Preferences
Authors: Alexander Gelbukh, Hiram Calvo
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-319-74054-6
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing AG 2018
Hardcover ISBN: 978-3-319-74053-9Published: 09 March 2018
Softcover ISBN: 978-3-030-08908-5Published: 01 February 2019
eBook ISBN: 978-3-319-74054-6Published: 28 February 2018
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: VIII, 165
Topics: Computational Intelligence, Computational Linguistics, Natural Language Processing (NLP), Artificial Intelligence