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Future and Emergent Trends in Language Technology

Overview of attention for book
Cover of 'Future and Emergent Trends in Language Technology'

Table of Contents

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    Book Overview
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    Chapter 1 Motivating New Interaction Experiences Involving Implicit Interaction, Body Sensors, Adaptive, and Persuasive Interfaces
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    Chapter 2 The SENSEI Project: Making Sense of Human Conversations
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    Chapter 3 Speech and Dialogue Technologies, Assets for the Multilingual Digital Single Market
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    Chapter 4 Helping Domain Experts Build Phrasal Speech Translation Systems
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    Chapter 5 A Speech-to-Speech, Machine Translation Mediated Map Task: An Exploratory Study
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    Chapter 6 Combining Several User Models to Improve and Adapt the Dialog Management Process in Spoken Dialog Systems
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    Chapter 7 Exploring Random Indexing for Profile Learning
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    Chapter 8 Text Categorisation by Using Sentiment Composition
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    Chapter 9 An Approach to Sentiment Analysis for Mobile Speech Applications
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    Chapter 10 Semantics for Social Media
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    Chapter 11 Semantic Similarity Reasoning
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    Chapter 12 Adopting Semantic Similarity for Utterance Candidates Discovery from Human-to-Human Dialogue Corpus
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    Chapter 13 Modelling Goal Modifications in User Simulation
Attention for Chapter 11: Semantic Similarity Reasoning
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Summary
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Chapter title
Semantic Similarity Reasoning
Chapter number 11
Book title
Future and Emergent Trends in Language Technology
Published in
Lecture notes in computer science, April 2016
DOI 10.1007/978-3-319-33500-1_11
Book ISBNs
978-3-31-933499-8, 978-3-31-933500-1
Authors

Luigi Di Caro, Guido Boella

Editors

José F. Quesada, Francisco-Jesús Martín Mateos, Teresa Lopez-Soto

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Italy 1 33%
Unknown 2 67%

Demographic breakdown

Readers by professional status Count As %
Professor 1 33%
Researcher 1 33%
Lecturer 1 33%
Readers by discipline Count As %
Computer Science 2 67%
Arts and Humanities 1 33%

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