Empirical Methods in Natural Language Generation : Data-oriented Methods and Empirical Evaluation / edited by Emiel Krahmer, Mariet Theune.

Format:

Book

Contributor:

Krahmer, Emiel, 1968- editor.

Theune, Mariët, 1971- editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence ; 5790.

Lecture Notes in Artificial Intelligence ; 5790

Language:

English

Subjects (All):

Natural language processing (Computer science).

Artificial intelligence.

Information storage and retrieval.

Application software.

Database management.

Data mining.

Natural Language Processing (NLP).

Artificial Intelligence.

Information Storage and Retrieval.

Information Systems Applications (incl. Internet).

Database Management.

Data Mining and Knowledge Discovery.

Local Subjects:

Natural Language Processing (NLP).

Artificial Intelligence.

Information Storage and Retrieval.

Information Systems Applications (incl. Internet).

Database Management.

Data Mining and Knowledge Discovery.

Physical Description:

1 online resource (X, 353 pages) : 82 illustrations.

Edition:

First edition 2010.

Contained In:

Springer eBooks

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2010.

System Details:

text file PDF

Summary:

Natural language generation (NLG) is a subfield of natural language processing (NLP) that is often characterized as the study of automatically converting non-linguistic representations (e.g., from databases or other knowledge sources) into coherent natural language text. In recent years the field has evolved substantially. Perhaps the most important new development is the current emphasis on data-oriented methods and empirical evaluation. Progress in related areas such as machine translation, dialogue system design and automatic text summarization and the resulting awareness of the importance of language generation, the increasing availability of suitable corpora in recent years, and the organization of shared tasks for NLG, where different teams of researchers develop and evaluate their algorithms on a shared, held out data set have had a considerable impact on the field, and this book offers the first comprehensive overview of recent empirically oriented NLG research.

Contents:

Text-to-Text Generation

Probabilistic Approaches for Modeling Text Structure and Their Application to Text-to-Text Generation

Spanning Tree Approaches for Statistical Sentence Generation

On the Limits of Sentence Compression by Deletion

NLG in Interaction

Learning Adaptive Referring Expression Generation Policies for Spoken Dialogue Systems

Modelling and Evaluation of Lexical and Syntactic Alignment with a Priming-Based Microplanner

Natural Language Generation as Planning under Uncertainty for Spoken Dialogue Systems

Referring Expression Generation

Generating Approximate Geographic Descriptions

A Flexible Approach to Class-Based Ordering of Prenominal Modifiers

Attribute-Centric Referring Expression Generation

Evaluation of NLG

Assessing the Trade-Off between System Building Cost and Output Quality in Data-to-Text Generation

Human Evaluation of a German Surface Realisation Ranker

Structural Features for Predicting the Linguistic Quality of Text

Towards Empirical Evaluation of Affective Tactical NLG

Shared Task Challenges for NLG

Introducing Shared Tasks to NLG: The TUNA Shared Task Evaluation Challenges

Generating Referring Expressions in Context: The GREC Task Evaluation Challenges

The First Challenge on Generating Instructions in Virtual Environments.

Other Format:

Printed edition:

ISBN:

978-3-642-15573-4

9783642155734

Access Restriction:

Restricted for use by site license.