Automatic Syntactic Analysis Based on Selectional Preferences

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.

List of contents

Introduction.- 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.

Summary

Describes recent methods to automatically analyze 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
Describes new contributions for automatic selectional preferences extraction and its multiple applications

Product details

• Authors: Hiram Calvo, Alexande Gelbukh, Alexander Gelbukh
• Publisher: Springer, Berlin
• Languages: English
• Product format: Hardback
• Released: 01.01.2018
• EAN: 9783319740539
• ISBN: 978-3-31-974053-9
• No. of pages: 165
• Dimensions: 161 mm x 243 mm x 16 mm
• Weight: 420 g
• Illustrations: VIII, 165 p.
• Series: Studies in Computational Intelligence; Studies in Computational Intelligence
• Subjects: Natural sciences, medicine, IT, technology > Technology > General, dictionaries; B, Artificial Intelligence, engineering, Computational Intelligence, Computational Linguistics, Natural language & machine translation, Natural Language Processing (NLP), Natural language processing (Computer science), Natural language and machine translation