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Automatic Text Simplification [Pehme köide]

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  • Formaat: Paperback / softback, 137 pages, kõrgus x laius: 235x191 mm, kaal: 280 g
  • Sari: Synthesis Lectures on Human Language Technologies
  • Ilmumisaeg: 25-Apr-2017
  • Kirjastus: Morgan and Claypool Life Sciences
  • ISBN-10: 1627058680
  • ISBN-13: 9781627058681
  • Formaat: Paperback / softback, 137 pages, kõrgus x laius: 235x191 mm, kaal: 280 g
  • Sari: Synthesis Lectures on Human Language Technologies
  • Ilmumisaeg: 25-Apr-2017
  • Kirjastus: Morgan and Claypool Life Sciences
  • ISBN-10: 1627058680
  • ISBN-13: 9781627058681
Thanks to the availability of texts on the Web in recent years, increased knowledge and information have been made available to broader audiences. However, the way in which a text is written--its vocabulary, its syntax--can be difficult to read and understand for many people, especially those with poor literacy, cognitive or linguistic impairment, or those with limited knowledge of the language of the text. Texts containing uncommon words or long and complicated sentences can be difficult to read and understand by people as well as difficult to analyze by machines.

Automatic text simplification is the process of transforming a text into another text which, ideally conveying the same message, will be easier to read and understand by a broader audience. The process usually involves the replacement of difficult or unknown phrases with simpler equivalents and the transformation of long and syntactically complex sentences into shorter and less complex ones.

Automatic text simplification, a research topic which started 20 years ago, now has taken on a central role in natural language processing research not only because of the interesting challenges it posesses but also because of its social implications. This book presents past and current research in text simplification, exploring key issues including automatic readability assessment, lexical simplification, and syntactic simplification. It also provides a detailed account of machine learning techniques currently used in simplification, describes full systems designed for specific languages and target audiences, and offers available resources for research and development together with text simplification evaluation techniques.
Acknowledgments xv
1 Introduction
1(6)
1.1 Text Simplification Tasks
1(1)
1.2 How are Texts Simplified?
2(1)
1.3 The Need for Text Simplification
3(2)
1.4 Easy-to-read Material on the Web
5(1)
1.5 Structure of the Book
6(1)
2 Readability and Text Simplification
7(14)
2.1 Introduction
7(1)
2.2 Readability Formulas
8(1)
2.3 Advanced Natural Language Processing for Readability Assessment
9(5)
2.3.1 Language Models
10(1)
2.3.2 Readability as Classification
10(2)
2.3.3 Discourse, Semantics, and Cohesion in Assessing Readability
12(2)
2.4 Readability on the Web
14(1)
2.5 Are Classic Readability Formulas Correlated?
15(1)
2.6 Sentence-level Readability Assessment
16(2)
2.7 Readability and Autism
18(1)
2.8 Conclusion
19(1)
2.9 Further Reading
19(2)
3 Lexical Simplification
21(12)
3.1 A First Approach
21(1)
3.2 Lexical Simplification in LexSiS
22(2)
3.3 Assessing Word Difficulty
24(1)
3.4 Using Comparable Corpora
25(1)
3.4.1 Using Simple English Wikipedia Edit History
25(1)
3.4.2 Using Wikipedia and Simple Wikipedia
25(1)
3.5 Language Modeling for Lexical Simplification
26(2)
3.6 Lexical Simplification Challenge
28(1)
3.7 Simplifying Numerical Expressions in Text
29(1)
3.8 Conclusion
30(1)
3.9 Further Reading
31(2)
4 Syntactic Simplification
33(14)
4.1 First Steps in Syntactic Simplification
33(1)
4.2 Syntactic Simplification and Cohesion
34(2)
4.3 Rule-based Syntactic Simplification using Syntactic Dependencies
36(1)
4.4 Pattern Matching over Dependencies with JAPE
37(3)
4.5 Simplifying Complex Sentences by Extracting Key Events
40(3)
4.6 Conclusion
43(1)
4.7 Further Reading
44(3)
5 Learning to Simplify
47(12)
5.1 Simplification as Translation
47(2)
5.1.1 Learning Simple English
48(1)
5.1.2 Facing Strong Simplifications
49(1)
5.2 Learning Sentence Transformations
49(6)
5.3 Optimizing Rule Application
55(2)
5.4 Learning from a Semantic Representation
57(1)
5.5 Conclusion
58(1)
5.6 Further Reading
58(1)
6 Full Text Simplification Systems
59(12)
6.1 Text Simplification in PSET
59(1)
6.2 Text Simplification in Simplext
60(7)
6.2.1 Rule-based "Lexical" Simplification
63(1)
6.2.2 Computational Grammars for Simplification
64(3)
6.2.3 Evaluating Simplext
67(1)
6.3 Text Simplification in PorSimples
67(3)
6.3.1 An Authoring Tool with Simplification Capabilities
69(1)
6.4 Conclusion
70(1)
6.5 Further Reading
70(1)
7 Applications of Automatic Text Simplification
71(8)
7.1 Simplification for Specific Target Populations
71(2)
7.1.1 Automatic Text Simplification for Reading Assistance
71(1)
7.1.2 Simplification for Dyslexic Readers
72(1)
7.1.3 Simplification-related Techniques for People with Autism Spectrum Disorder
72(1)
7.1.4 Natural Language Generation for Poor Readers
73(1)
7.2 Text Simplification as NLP Facilitator
73(3)
7.2.1 Simplification for Parsing
73(1)
7.2.2 Simplification for Information Extraction
74(1)
7.2.3 Simplification in and for Text Summarization
74(1)
7.2.4 Simplifying Medical Literature
75(1)
7.2.5 Retrieving Facts from Simplified Sentences
75(1)
7.2.6 Simplifying Patent Documents
76(1)
7.3 Conclusion
76(1)
7.4 Further Reading
77(2)
8 Text Simplification Resources and Evaluation
79(16)
8.1 Lexical Resources for Simplification Applications
79(1)
8.2 Lexical Simplification Resources
80(3)
8.3 Corpora
83(3)
8.4 Non-English Text Simplification Datasets
86(4)
8.5 Evaluation
90(2)
8.6 Toward Automatically Measuring the Quality of Simplified Output
92(1)
8.7 Conclusion
93(1)
8.8 Further Reading
93(2)
9 Conclusion
95(2)
Bibliography 97(24)
Author's Biography 121