Cognitive approach to natural language processing
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2017
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| 245 | 1 | 0 | |a Cognitive approach to natural language processing |c edited by Bernadette Sharp, Florence Sèdes, Wiesław Lubaszewski |
| 264 | 1 | |a London |b ISTE Press |c 2017 | |
| 300 | |a xiii, 220 Seiten |b Illustrationen, Diagramme | ||
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| 700 | 1 | |a Sharp, Bernadette |0 (DE-588)173661769 |4 edt | |
| 700 | 1 | |a Sèdes, Florence |0 (DE-588)1139725777 |4 edt | |
| 700 | 1 | |a Lubaszewski, Wiesław |d ca. 20./21. Jh. |0 (DE-588)124943615X |4 edt | |
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Datensatz im Suchindex
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| adam_text | Contents
Preface................................................................... xi
Chapter 1. Delayed Interpretation, Shallow
Processing and Constructions: the Basis
of the “Interpret Whenever Possible” Principle............................ 1
Philippe Blache
1.1. Introduction...................................................... 1
1.2. Delayed processing................................................ 3
1.3. Working memory.................................................... 5
1.4. How to recognize chunks: the
segmentation operations................................................ 8
1.5. The delaying architecture........................................ 10
1.5.1. Segment-and-store............................................ 11
1.5.2. Aggregating by cohesion ..................................... 12
1.6. Conclusion..................................................... 16
1.7. Bibliography..................................................... 17
Chapter 2. Can the Human Association Norm
Evaluate Machine-Made Association Lists?.................................. 21
Michał Korzycki, Izabela Gatkowska
and Wiesław Lubaszewski
2.1. Introduction..................................................... 21
2.2. Human semantic associations...................................... 23
2.2.1. Word association test........................................ 23
2.2.2. The author’s experiment. .................................. 24
2.2.3. Human association topology................................... 25
2.2.4. Human associations are comparable............................ 26
vi Cognitive Approach to Natural Language Processing
2.3. Algorithm efficiency comparison................................. 29
2.3.1. The corpora................................................... 29
2.3.2. LSA-sourced association lists................................. 29
2.3.3. LDA-sourced lists............................................. 31
2.3.4. Association ratio-based lists................................. 31
2.3.5. List comparison............................................... 32
2.4. Conclusion........................................................ 37
2.5. Bibliography.................................................. 38
Chapter 3. How a Word of a Text Selects the
Related Words in a Human Association Network............................... 41
Wiesław Lubaszewski, Izabela Gatkowska
and Maciej Godny
3.1. Introduction...................................................... 41
3.2. The network..........................................-......... 44
3.3. The network extraction driven by a
text-based stimulus.................................................. 46
3.3.1. Sub-graph extraction algorithm................................ 46
3.3.2. The control procedure.......................................... 48
33.3. The shortest path extraction................................... 48
3.3.4. A corpus-based sub-graph....................................... 50
3.4. Tests of the network extracting procedure......................... 50
3.4. L The corpus to perform tests................................. 50
3.4.2. Evaluation of the extracted sub-graph.......................... 51
3.4.3. Directed and undirected sub-graph
extraction: the comparison ........................................... 52
3.4.4. Results per stimulus........................................... 53
3.5. A brief discussion of the results and
the related work........................................................ 58
3.6. Bibliography...................................................... 60
Chapter 4. The Reverse Association Task..................................... 63
Reinhard Rapp
4.1. Introduction....................................................... 63
4.2. Computing forward associations..................................... 67
4.2.1. Procedure...................................................... 67
4.2.2. Results and evaluation......................................... 69
4.3. Computing reverse associations..................................... 71
4.3.1. Problem........................................................ 71
43.2. Procedure...................................................... 71
4.3.3. Results and evaluation......................................... 76
Contents vii
4.4. Human performance................................................ 78
4.4.1. Dataset...................................................... 78
4.4.2. Test procedure............................................... 80
4.4.3. Evaluation................................................... 81
4.5. Performance by machine........................................... 82
4.6. Discussion, conclusions and outlook.............................. 84
4.6.1. Reverse associations by a human.............................. 84
4.6.2. Reverse associations by a machine............................ 85
4.7. Acknowledgments.................................................. 87
4.8. Bibliography..................................................... 88
Chapter 5. Hidden Structure and
Function in the Lexicon............................................... 91
Philippe Vincent-Lamarre, Melanie Lord,
Alexandre Blondin-Masse, Odile Marcotte,
Marcos Lopes and Stevan Harnad
5.1. Introduction..................................................... 91
5.2. Methods.......................................................... 92
5.2.1. Dictionary graphs............................................ 92
5.2.2. Psycholinguistic variables................................... 96
5.2.3. Data analysis................................................ 96
5.3. Psycholinguistic properties of Kernel, Satellites,
Core, MinSets and the rest of each dictionary......................... 97
5.4. Discussion...................................................... 101
5.4.1. Limitations................................................. 104
5.5. Future work..................................................... 104
5.6. Bibliography.................................................... 106
Chapter 6. Transductive Learning Games
for Word Sense Disambiguation........................................... 109
Rocco Tripodi and Marcello Pelillo
6.1. introduction.................................................... 109
6.2. Graph-based word sense disambiguation........................... Ill
6.3. Our approach to semi-supervised learning........................ 113
6.3.1. Graph-based semi-supervised learning........................ 113
6.3.2. Game theory and game dynamics............................... 114
6.4. Word sense disambiguation games................................. 116
6.4.1. Graph construction.......................................... 116
6.4.2. Strategy space.............................................. 117
6.4.3. The payoff matrix........................................... 118
6.4.4. System dynamics........................................... 119
viii Cognitive Approach to Natura! Language Processing
6.5. Evaluation...................................................... 120
6.5.1. Experimental setting........................................ 120
6.5.2. Evaluation results................ ......................... 121
6.5.3. Comparison with state-of-the-art algorithms................. 124
6.6. Conclusion...................................................... 124
6.7. Bibliography.................................................... 125
Chapter 7. Use Your Mind and Learn to
Write: The Problem of Producing Coherent Text............................ 129
Michael ZOCK and Debela Tesfaye Gemechu
7.1. The problem..................................................... 129
7.2. Suboptimal texts and some of the reasons........................ 131
7.2.1. Lack of coherence or cohesion................................ 132
7.2.2. Faulty reference............................................ 133
7.2.3. Unmotivated topic shift..................................... 134
7.3. How to deal with the complexity of the task?.................... 135
7.4. Related work....................................................... 136
7.5. Assumptions concerning the building of
a tool assisting the writing process................................. 138
7.6. Methodology..................................................... 141
7.6.1. Identification of the syntactic structure ................. 143
7.6.2. Identification of the semantic seed words................... 144
7.6.3. Word alignment.............................................. 145
7.6.4. Determination of the similarity
values of the aligned words........................................ . 146
7.6.5. Determination of the similarity
between sentences............................................. 150
7.6.6. Sentence clustering based on
their similarity values............................................ 151
7.7. Experiment and evaluation.......................................... 151
7.8. Outlook and conclusion............................................. 154
7.9. Bibliography..................................................... 155
Chapter 8. Stylistic Features Based on
Sequential Rule Mining for Authorship Attribution.................... 159
Mohamed Amine BOUKHALED and Jean-Gabriel GANASCIA
8.1. Introduction and motivation...................................... 159
8.2. The authorship attribution process................................. 162
8.3. Stylistic features for authorship attribution................... 163
8.4. Sequential data mining for stylistic analysis................... 165
N.
Contents ix
8.5. Experimental setup............................................. 166
8.5.1. Dataset.................................................... 166
8.5.2. Classification scheme...................................... 167
8.6. Results and discussion......................................... 169
8.7. Conclusion..................................................... 173
8.8. Bibliography................................................... 173
Chapter 9. A Parallel, Cognition-oriented
Fundamental Frequency Estimation Algorithm........................... 177
Ulrike Glavitsch
9.1. introduction................................................... 177
9.2. Segmentation of the speech signal.............................. 180
9.2.1. Speech and pause segments.................................. 180
9.2.2. Voiced and unvoiced regions................................ 182
9.2.3. Stable and unstable intervals.............................. 183
9.3. F0 estimation for stable intervals............................. 184
9.4. F0 propagation................................................. 186
9.4.1. Control flow............................................... 187
9.4.2. Peak propagation........................................... 189
9.5. Unstable voiced regions........................................ 191
9.6. Parallelization................................................ 191
9.7. Experiments and results........................................ 192
9.8. Conclusions.................................................... 194
9.9. Acknowledgments................................................ 195
9.10. Bibliography.................................................. 195
Chapter 10. Benchmarking n-grams,
Topic Models and Recurrent Neural Networks
by Cloze Completions, EEGs and Eye Movements........................... 197
Markus J. Hofmann, Chris Biemann and Steffen Remus
10.1. Introduction.................................................. 198
10.2. Related work.................................................. 199
10.3. Methodology................................................... 2^9
10.3.1. Human performance measures................................ 200
10.3.2. Three flavors of language models.......................... 201
10.4. Experiment setup.............................................. 203
10.5. Results....................................................... 204
10.5.1. Predictability results.................................... 204
10.5.2. N400 amplitude results.................................... 206
10.5.3. Single-fixation duration (SFD) results.................... 208
x Cognitive Approach to Natural Language Processing
10.6. Discussion and conclusion................................. 210
10.7. Acknowledgments........................................... 212
10.8. Bibliography.............................................. 212
List of Authors.................................................... 217
Index.............................................................. 219
As natural language processing spans many different disciplines, it is sometimes
difficult to understand the contributions and the challenges that each of them
presents. This book explores the special relationship between natural language
processing and cognitive science, and the contribution of computer science to
these two fields. It is based on the recent research papers submitted at the
international workshops of Natural Language and Cognitive Science (NLPCS)
which was launched in 2004 in an effort to bring together natural language
researchers, computer scientists, and cognitive and linguistic scientists to
collaborate together and advance research in natural language processing.
The chapters cover areas related to language understanding, language
generation, word association, word sense disambiguation, word predictability,
text production and authorship attribution. This book will be relevant to students
and researchers interested in the interdisciplinary nature of language processing.
Bernadette Sharp is Professor of Applied Artificial Intelligence (Al) at
Staffordshire University, UK. Her research interests include Al, natural language
processing, and text mining. She has been Chair and Editor of the International
Workshop for Natural Language Processing and Cognitive Science since 2004.
Florence Sedes is Professor of Computer Science at Toulouse University, France.
Her research areas cover information systems and data management with
applications dedicated to multimedia, metadata and mobility in ambient
intelligence, social media and CCTV. She supervises a smart restaurant
platform for emotion and social interaction analysis, and contributes to the ISO
22311 standard.
Wiesław Lubaszewski is Professor at the Department of Computational
Linguistics of the Jagiellonian University and Professor at the Computer Science
Department of AGH, University of Technology, in Kraków, Poland. His research
interests include natural language dictionaries, text understanding, knowledge
representation, and information extraction.
|
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| spellingShingle | Cognitive approach to natural language processing Kognitionswissenschaft (DE-588)4193780-6 gnd Sprachverarbeitung (DE-588)4116579-2 gnd |
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| title | Cognitive approach to natural language processing |
| title_auth | Cognitive approach to natural language processing |
| title_exact_search | Cognitive approach to natural language processing |
| title_full | Cognitive approach to natural language processing edited by Bernadette Sharp, Florence Sèdes, Wiesław Lubaszewski |
| title_fullStr | Cognitive approach to natural language processing edited by Bernadette Sharp, Florence Sèdes, Wiesław Lubaszewski |
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| title_short | Cognitive approach to natural language processing |
| title_sort | cognitive approach to natural language processing |
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