Cross-Disciplinary Advances in Applied Natural Language Processing: Issues and Approaches: Issues and Approaches

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Boonthum-Denecke, Chutima
IGI Global, Dec 31, 2011 - Computers - 438 pages
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Applied Natural Language Processing (ANLP) is interested in not only the creation of natural language processing approaches (i.e., tools, systems, algorithms, models, theories, and techniques), but it is also (and, arguably, more specifically) interested in how those approaches stack up against new problems, issues, identified knowledge gaps, or created data sets.

Cross-Disciplinary Advances in Applied Natural Language Processing: Issues and Approaches defines the role of ANLP within NLP, and alongside other disciplines such as linguistics, computer science, and cognitive science. The description also includes the categorization of current ANLP research, and examples of current research in ANLP. This book is a useful reference for teachers, students, and materials developers in fields spanning linguistics, computer science, and cognitive science.

 

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Contents

Computational Semantics Requires Computation
1
Natural Language Processing Tools
9
Information Extraction from Text and Beyond
24
Corpora and Concordancers
40
Lexical Challenges in the Intersection of Applied Linguistics and ANLP
50
The UserLanguage Paraphrase Corpus
73
Amazon Mechanical Turk
90
Some Issues on Capturing the Meaning of Negated Statements
103
Newness and Givenness of Information
202
Improving Spoken Dialogue Understanding Using Phonetic Mixture Models
225
Mining and Visualizing the Narration Tree of Hadiths Prophetic Traditions
239
Combinatory Categorial Grammar for ComputerAssisted Language Learning
258
Fairy Tales and ESL Texts
273
TextBased Affect Detection in Intelligent Tutors
284
Multiple Influences on the Use of English Spatial Prepositions
305
Evaluating Semantic Metrics on Tasks of Concept Similarity
324

Cognitive Load Aspects of Text Processing
114
Understanding and Reasoning with Text
133
Section 2
155
Guru
156
PMATCH
172
Disambiguation and Filtering Methods in Using Web Knowledge for Coreference Resolution
185
Extracting Commonsense Knowledge Using Concepts Properties
341
CoOccurrenceBased Error Correction Approach to Word Segmentation
354
Compilation of References
365
About the Contributors
401
Index
410
Copyright

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About the author (2011)

Chutima Boonthum-Denecke is an Assistant Professor in the Department of Computer Science at Hampton University. Dr. Boonthum-Denecke earned her Ph.D. in Computer Science from Old Dominion in 2007; MS in Applied Computer Science from Illinois State University in 2000; and BS in Computer Science from Srinakharinwirot University in 1997. Dr. Boonthum-Denecke has been involved in several NSF-funded Broadening Participation in Computing (BPC) programs: ARTSI (Advancing Robotics Technology for Societal Impact) and STARS (Students and Technology in Academia, Research and Service) Alliances. She is also a faculty member of the Hampton University Information Assurance group. Dr. Boonthum-Denecke's research interests include artificial intelligence (natural language processing, computational linguistics), information retrieval, Web development technology, and cognitive robotics.

Philip M. McCarthy is an Assistant Professor at The University of Memphis. He is also a member of the Institute for Intelligent Systems. His research is in the field of Applied Natural Language Processing (ANLP). His primary interest is devising algorithms for contrastive corpus analyses, particularly where such algorithms can be informative of the relationship between language, the mind, and the world.

Travis A. Lamkin is a graduate student at The University of Memphis, in the Applied Linguistics program. His primary research area is Applied Natural Language Processing (ANLP). He is currently involved in the Gramulator Project. He is particularly interested in applying methods and tools, such as the Gramulator, to the studies of literary criticism and genre assessment. [Editor]

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