
Contents
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47.1 Introduction 47.1 Introduction
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47.2 A Few Words about Readability 47.2 A Few Words about Readability
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47.2.1 Readability Assessment the NLP Way 47.2.1 Readability Assessment the NLP Way
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47.2.2 Using Language Models 47.2.2 Using Language Models
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47.3 Lexical Simplification Approaches 47.3 Lexical Simplification Approaches
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47.3.1 Using Comparable Corpora 47.3.1 Using Comparable Corpora
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47.3.2 Language Modelling for Lexical Simplification 47.3.2 Language Modelling for Lexical Simplification
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47.4 Syntactic Simplification 47.4 Syntactic Simplification
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47.4.1 Text Simplification and Cohesion 47.4.1 Text Simplification and Cohesion
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47.4.2 Syntatic Dependencies for Richer Representations 47.4.2 Syntatic Dependencies for Richer Representations
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47.4.3 Syntactic Simplification for Specific Users 47.4.3 Syntactic Simplification for Specific Users
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47.5 Machine Learning Techniques for Text Simplification 47.5 Machine Learning Techniques for Text Simplification
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47.5.1 Learning Sentence Transformations 47.5.1 Learning Sentence Transformations
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47.6 Open Issues in Text Simplification 47.6 Open Issues in Text Simplification
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Further Reading and Relevant Resources Further Reading and Relevant Resources
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References References
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47 Text Simplification
Get accessHoracio Saggion is Associate Professor at the Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, and Head of the Large Scale Text Understanding Systems Lab. He obtained his PhD diploma in computer science from DIRO, University of Montreal, Canada. Before joining Universitat Pompeu Fabra, he had an appointment as senior researcher at the University of Sheffield, where he developed cutting-edge, competitive human language technology. He has published over 150 works in leading scientific journals, conferences, and books in the field of human language technology.
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Published:05 February 2018
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Abstract
Over the past decades, information has been made available to a broad audience thanks to the availability of texts on the Web. However, understanding the wealth of information contained in texts can pose difficulties for a number of people including those with poor literacy, cognitive or linguistic impairment, or those with limited knowledge of the language of the text. Text simplification was initially conceived as a technology to simplify sentences so that they would be easier to process by natural-language processing components such as parsers. However, nowadays automatic text simplification is conceived as a technology to transform a text into an equivalent which is easier to read and to understand by a target user. Text simplification concerns both the modification of the vocabulary of the text (lexical simplification) and the modification of the structure of the sentences (syntactic simplification). In this chapter, after briefly introducing the topic of text readability, we give an overview of past and recent methods to address these two problems. We also describe simplification applications and full systems also outline language resources and evaluation approaches.
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