E-Book, Englisch, Band 20, 346 Seiten
Qu / Wiebe Computing Attitude and Affect in Text: Theory and Applications
1. Auflage 2006
ISBN: 978-1-4020-4102-0
Verlag: Springer Netherlands
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)
E-Book, Englisch, Band 20, 346 Seiten
Reihe: The Information Retrieval Series
ISBN: 978-1-4020-4102-0
Verlag: Springer Netherlands
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)
Human Language Technology (HLT) and Natural Language Processing (NLP) systems have typically focused on the "factual" aspect of content analysis. Other aspects, including pragmatics, opinion, and style, have received much less attention. However, to achieve an adequate understanding of a text, these aspects cannot be ignored. The chapters in this book address attitude, affect, and subjective opinion. Various conceptual models and computational methods are presented, including distinguishing attitudes from simple factual assertions; distinguishing between the author?s reports from reports of other people?s opinions; and distinguishing between explicitly and implicitly stated attitudes. In addition, many applications are described that promise to benefit from the ability to understand attitudes and affect, such as indexing and retrieval of documents by opinion; automatic question answering about opinions; analysis of sentiment in the media and in discussion groups; analyzing client discourse in therapy and counseling; determining relations between scientific texts; generating more appropriate texts; and creating writers? aids. In addition to English texts, the collection includes studies of French, Japanese, and Portuguese texts. The chapters in this book are extended and revised versions of papers presented at the American Association for Artificial Intelligence (AAAI) Spring Symposium on Exploring Attitude and Affect in Text, which took place in March 2004 at Stanford University. The symposium, and the book which grew out it, represents a first foray into this area and a balance among conceptual models, computational methods, and applications.
Zielgruppe
Computer Science, general, Information Systems Applications (incl.Internet), Artificial Intelligence (incl. Robotics), Language Translation and Linguistics, Computer Applications, Computational Linguistics
Autoren/Hrsg.
Fachgebiete
- Technische Wissenschaften Technik Allgemein Computeranwendungen in der Technik
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Spracherkennung, Sprachverarbeitung
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
- Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Geistes- und Sozialwissenschaften
- Mathematik | Informatik EDV | Informatik EDV & Informatik Allgemein
- Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie
- Geisteswissenschaften Sprachwissenschaft Computerlinguistik, Korpuslinguistik
- Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Information Retrieval
- Mathematik | Informatik EDV | Informatik Technische Informatik Systemverwaltung & Management
Weitere Infos & Material
1;TABLE OF CONTENTS;6
2;Preface;12
3;Contextual Valence Shifters;18
3.1;1. Introduction;18
3.2;2. From Simple Valence to Contextually Determined Valence;19
3.3;3. Contextual Valence Shifters;20
3.4;4. Conclusion;26
3.5;5. Bibliography;27
4;Conveying Attitude with Reported Speech;28
4.1;1. Introduction;28
4.2;2. Evidential Analysis of Reported Speech;29
4.3;3. Profile Structure;31
4.4;4. Extended Example;33
4.5;5. Source List Annotation;34
4.6;6. Extension to Other Attribution;37
4.7;7. Conclusion;37
4.8;8. Acknowledgements;38
4.9;9. Bibliography;38
5;Where Attitudinal Expressions Get their Attitude;40
5.1;1. Research Questions to Motivate the Study of Attitudinal Expressions;40
5.2;2. Starting Points – Prototypical Attitudinal Expressions;41
5.3;3. Text Topicality: Players;41
5.4;4. Text Topicality: Moves;42
5.5;5. Identifying Players;42
5.6;6. The Case for Animacy: Adjectival Attributes and Genitive Attributes;42
5.7;7. The Case for Syntactic Structure: Situational Reference;43
5.8;8. Using Syntactic Patterns more Systematically;45
5.9;9. Generalizing from Syntactic Patterns to the Lexicon;46
5.10;10. Conclusions;46
5.11;11. Bibliography;47
6;The Subjectivity of Lexical Cohesion in Text;56
6.1;1. Introduction;56
6.2;2. Theoretical Background;57
6.3;3. Experimental Study;58
6.4;4. Discussion;60
6.5;5. Acknowledgements;61
6.6;6. Bibliography;61
7;A Weighted Referential Activity Dictionary;63
7.1;1. Introduction;64
7.2;2. Methods;66
7.3;3. Results;72
7.4;4. Bibliography;74
8;Certainty Identification in Texts: Categorization Model and Manual Tagging Results;75
8.1;1. Analytical Framework;76
8.2;2. Proposed Certainty Categorization Model;79
8.3;3. Empirical Study;82
8.4;4. Applications;88
8.5;5. Conclusions and Future Work;88
8.6;6. Acknowledgements;89
8.7;7. Bibliography;89
9;Evaluating an Opinion Annotation Scheme Using a New Multi-Perspective Question and Answer Corpus;91
9.1;1. Introduction;91
9.2;2. Low-Level Perspective Information;92
9.3;3. The MPQA NRRC Corpus;94
9.4;4. Multi-Perspective Question and Answer Corpus Creation;94
9.5;5. Evaluation of Perspective Annotations for MPQA;97
9.6;6. Conclusions and Future Work;103
9.7;7. Acknowledgements;103
9.8;8. Bibliography;104
10;A Computational Semantic Lexicon of French Verbs of Emotion;121
10.1;1. Introduction;121
10.2;2. Semantic Lexicon Description;121
10.3;3. FEELING System;128
10.4;4. Evaluation;134
10.5;5. Related Work;135
10.6;6. Conclusion;135
10.7;7. Bibliography;135
11;Extracting Opinion Propositions and Opinion Holders using Syntactic and Lexical Cues;137
11.1;1. Introduction;137
11.2;2. Data;139
11.3;3. Opinion-Oriented Words;142
11.4;4. Identifying Opinion Propositions;144
11.5;5. Results;148
11.6;6. Error Analysis;150
11.7;7. Discussion;151
11.8;8. Acknowledgments;152
11.9;9. Bibliography;152
12;Approaches for Automatically Tagging Affect: Steps Toward an Effective and Efficient Tool;154
12.1;1. Introduction;154
12.2;2. Background;155
12.3;3. Rochester Marriage-Counseling Corpus;156
12.4;4. Approaches to Tagging;157
12.5;5. Evaluations;164
12.6;6. Discussion;165
12.7;7. CATS Tool;167
12.8;8. Related Work;168
12.9;9. Conclusion;168
12.10;10. Acknowledgments;169
12.11;11. Bibliography;169
13;Argumentative Zoning for Improved Citation Indexing;170
13.1;1. Citation Indexing and Citation Maps;170
13.2;2. Argumentative Zoning and Author Affect;172
13.3;3. Meta-discourse;174
13.4;4. Human Annotation of Author Affect;176
13.5;5. Features for Author Affect;178
13.6;6. Evaluation;178
13.7;7. Conclusion;179
13.8;8. Bibliography;179
14;Two Exploratory Studies Politeness and Bias in Dialogue Summarization;181
14.1;1. Introduction;182
14.2;2. First Study: Politeness and Bias in Unconstrained Dialogue Summarization;184
14.3;3. Second Study: Politeness and Bias in Constrained Dialogue Summarization;188
14.4;4. Comparison;190
14.5;5. Conclusion and Outlook;191
14.6;6. Acknowledgements;192
14.7;7. Bibliography;192
14.8;8. Appendix I;195
15;Generating More-Positive and More-Negative Text;196
15.1;1. Near-Synonyms and Attitudinal Nuances;196
15.2;2. Related Work;198
15.3;3. Estimating the Relative Semantic Orientation of Text;198
15.4;4. Word Sense Disambiguation;199
15.5;5. Analysis;199
15.6;6. Generation;200
15.7;7. Experiments;201
15.8;8. Evaluation;204
15.9;9. Conclusion;205
15.10;10. Acknowledgements;205
15.11;11. Bibliography;205
16;Identifying Interpersonal Distance using Systemic Features;208
16.1;1. Introduction;209
16.2;2. Systemic Functional Linguistics;209
16.3;3. Representing System Networks;213
16.4;4. Identifying Registers;218
16.5;5. Conclusion;221
16.6;6. Bibliography;221
17;Corpus-Based Study of Scientific Methodology: Comparing the Historical and Experimental Sciences;224
17.1;1. Introduction;225
17.2;2. Background;225
17.3;3. Systemic Indicators as Textual Features;228
17.4;4. Experimental Study;231
17.5;5. Example Texts;236
17.6;6. Conclusions;237
17.7;7. Acknowledgements;237
17.8;8. References;237
18;Argumentative Zoning Applied to Critiquing Novices™ Scientific Abstracts;241
18.1;1. Introduction;242
18.2;2. The SciPo System;242
18.3;3. Argumentative Zoning for Portuguese Texts;245
18.4;4. Evaluation of SciPo™s Critiquing Tool;250
18.5;5. Conclusions;252
18.6;6. Acknowledgements;252
18.7;7. Bibliography;252
19;Using Hedges to Classify Citations in Scientific Articles;255
19.1;1. Scientific Writing, the Need for Affect, and Its Role in Citation Analysis;255
19.2;2. Hedging in Scientific Writing;256
19.3;3. Classifying Citations in Scientific Writing;258
19.4;4. Determining the Importance of Hedges in Citation Contexts;260
19.5;5. A Citation Indexing Tool for Biomedical Literature Analysis;264
19.6;6. Conclusions and Future Work;269
19.7;7. Acknowledgements;269
19.8;8. Bibliography;269
20;Towards a Robust Metric of Polarity;272
20.1;1. Introduction;272
20.2;2. Related Work;273
20.3;3. Classes of Polar Expression;275
20.4;4. Recognizing Polar Language;276
20.5;5. Topic Detection in Online Messages;277
20.6;6. The Intersection of Topic and Polarity;279
20.7;7. Empirical Analysis;280
20.8;8. Metrics for Topic and Polarity;282
20.9;9. Conclusions and Future Work;284
20.10;10. References;285
21;Characterizing Buzz and Sentiment in Internet Sources: Linguistic Summaries and Predictive Behaviors;287
21.1;1. Introduction and Motivation;287
21.2;2. Linguistic Summaries;288
21.3;3. Example Applications;295
21.4;4. TRENDS-2™ Infrastructure;298
21.5;5. Previous and Related Work;299
21.6;6. Open R&D and Application Issues;299
21.7;7. Bibliography;300
22;Good News or Bad News? Let the Market Decide;303
22.1;1. Introduction;303
22.2;2. Experiments;304
22.3;3. Results;305
22.4;4. Conclusions;306
23;Opinion Polarity Identification of Movie Reviews;308
23.1;1. Introduction;308
23.2;2. Related Research;309
23.3;3. Probabilistic Approaches to Polarity Identification;310
23.4;4. Features for Analysis;311
23.5;5. Part of Speech Feature Selection;312
23.6;6. Experiments;313
23.7;7. Synonymy and Hypernymy Feature Generalization;317
23.8;8. Selection by Ranking;319
23.9;9. Discussion;319
23.10;10. Conclusion;320
23.11;11. Acknowledgements;320
23.12;12. Bibliography;320
24;Multi-Document Viewpoint Summarization Focused on Facts, Opinion and Knowledge;322
24.1;1. Introduction;323
24.2;2. Experiment Overview: Multi-Document Viewpoint Summarization with Summary Types;324
24.3;3. Sentence-type Annotation;328
24.4;4. Genre Classification;330
24.5;5. Experiment Results;333
24.6;6. Conclusion;338
24.7;7. Acknowledgement;339
24.8;8. Bibliography;339
25;INDEX;342
