Contextual computing for natural language processing:
Saved in:
| Main Author: | |
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| Format: | Thesis Book |
| Language: | English |
| Published: |
2009
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis Inhaltsverzeichnis Inhaltsverzeichnis |
| Physical Description: | 200 S. Ill., graph. Darst. |
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MARC
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Record in the Search Index
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|---|---|
| adam_text | Contents
1 Intro duetion 13
1.1 Motivation .................................................... 14
1.2 Thesis Aim and Contribution.................................... 16
1.3 Thesis Organization............................................ 17
2 State of the Art 19
2.1 Defining Context............................................... 19
2.2 Fleshing Out Context .......................................... 22
2.3 Context in Language............................................ 25
2.4 Context in Natural Language Processing......................... 29
2.4.1 From Past to Present: The Historical Context............ 30
2.4.2 The Present: Multimodal Systems......................... 31
2.5 Methodological Background...................................... 34
2.5.1 Performance in Dialogue Systems Evaluations............. 34
2.5.2 Performance in Automatic Speech Recognition Evaluations 35
2.5.3 Performance in Understanding Evaluations................ 35
2.5.4 Performance in Classification Evaluations............... 37
2.6 Measuring Task Difficulties and Baselines...................... 38
2.6.1 Measuring Perplexity in Automatic Speech Recognition 38
2.6.2 Measuring Task-specific Baselines....................... 39
2.7 Point of Departure............................................. 40
2.7.1 Context Types........................................... 41
2.7.2 The Tasks (Revisited)................................... 41
3 Domain and Discourse 45
3.1 Modeling Domain and Discourse Knowledge........................ 45
3.1.1 Modeling Domains........................................ 45
3.1.2 Modeling Discourse ..................................... 48
3.1.3 Semantics in SmartKom................................... 50
3.1.4 Modeling Ground Knowledge............................... 51
3.1.5 Roadmap................................................. 55
3.2 Using Domain Context for Noisy Input........................... 56
3.2.1 The Task: Domain-sensitive Hypothesis Verification . . . 56
3.2.2 The Data: Collection L· Annotation...................... 58
3
4
CONTENTS
3.2.3 The Algorithm: Domam-specific. Coherence............. 63
3.2.4 Results: Domain-sensitive Hypothesis Verification .... 69
3.2.5 Roadmap.............................................. 74
3.3 Using Discourse Context for Noisy Input..................... 74
3.3.1 The Task: Discourse-sensitive Hypothesis Verification 75
3.3.2 The Data: Collection Sc Annotation .................. 75
3.3- 3 The Algorithm: Scoring cum Discourse ................ 76
3.3.4 The Results: Discourse-sensitive Hypothesis Verification . 77
3.3.5 Roadmap................................. ............ 83
3.4 Using Domain Context for Semantic Ambiguity.............. 83
3 4-1 The Task: Word Sense Disambiguation.................. 84
3.4.2 The Data; Collection Sc Annotation .................. 86
3.4- 3 The Algorithm: Scoring Word-Sense Ambiguities ...... 87
3.4.4 The Results: Word Sense Disambiguation............... 90
3.5 Using Domain Context for Relation Extraction............... „ . 92
3-5-1 The Task: Relation Extraction 93
3.5.2 The Data: Collection Sz Annotation .............„ . - 93
3.5.3 The Results: Relation Extraction . . . ............. - .. 96
3.6 Evaluating Domain Context..................................... 97
3- 6.1 The Task: Evaluating Ontological Fitness............... 98
3.6.2 The Data: An Evaluation Suite..........................103
3.6.3 The Results՜. Ontological Fitness......................106
3.7 Summing-up ................................................. 109
3.7.1 Roadmap............................................... Ц2
4 User and Situation 113
4.1 Modeling User and Situation ..................................114
41.1 Modeling the User......................................114
4.1.2 Modeling the Situation ................................119
4.1.3 Pragmatics in SmartKom.................................120
4- 1.4 Modeling Implicit Information .........................124
4.1.5 Roadmap................................................127
4.2 Using Situational Context for Underspecification............ 128
4.2.1 The Task: Pragmatic Disambiguation.....................129
4.2.2 The Data: Collection Sc Annotation.....................130
4.2.3 The Algorithm: Scoring Construais.................... 135
4.2.4 The Results: Pragmatic Ambiguity.......................137
4-3 Modeling What Matters....................................... 139
4.3.1 Ontological Choices Sc Patterns...................„ 140
4.3.2 Foundational and Ground Knowledge ........... 141
4-3.3 Logical- and Content Patterns ......................... , 142
4.4 Pragmatic Patterns........................................... 145
4-4Л Implementing Pragmatic Patterns...................... 147
4.4.2 Applying Pragmatic Patterns.......................... 149
4-4-3 Experimental Results: Decontextualization..............155
CONTENTS
5
5 Conclusion 161
51 Aims and Contributions......................................162
5.2 Future Work 163
5.3 Concluding Remarks..........................................166
Contents
1 Introduction 13
1.1 Motivation ................................................... 14
1.2 Thesis Aim and Contribution................................... 16
1.3 Thesis Organization........................................... 17
2 State of the Art 19
2.1 Defining Context ............................................. 19
2.2 Fleshing Out Context.......................................... 22
2.3 Context in Language........................................... 25
2.4 Context in Natural Language Processing........................ 29
2.4.1 FVom Past to Present: The Historical Context........... 30
2.4.2 The Present: Multimodal Systems........................ 31
2.5 Methodological Background . ■................................. 34
2.5.1 Performance in Dialogue Systems Evaluations............ 34
2.5.2 Performance in Automatic Speech Recognition Evaluations 35
2.5.3 Performance in Understanding Evaluations............... 35
2.5.4 Performance in Classification Evaluations.............. 37
2.6 Measuring Task Difficulties and Baselines..................... 38
2.6.1 Measuring Perplexity in Automatic Speech Recognition 38
2.6.2 Measuring Task-specific Baselines...................... 39
2.7 Point of Departure............................................ 40
2.7.1 Context Types.......................................... 41
2.7.2 The Tasks (Revisited).................................. 41
3 Domain and Discourse 45
3.1 Modeling Domain and Discourse Knowledge....................... 45
3.1.1 Modeling Domains....................................... 45
3.1.2 Modeling Discourse .................................... 48
3.1.3 Semantics in Smart Korn................................ 50
3.1.4 Modeling Ground Knowledge.............................. 51
3.1.5 Roadmap................................................ 55
3.2 Using Domain Context for Noisy Input......................... 56
3.2.1 The Task: Domain-sensitive Hypothesis Verification ... 56
3.2.2 The Data: Collection Annotation...................... 58
3
4
CONTENTS
3.2,3 The Algorithm: Domain-specific Coherence................. 63
3-2.4 Results; Domain-sensitive Hypothesis Verification ... - 69
3.2.5 Roadmap................................................. 74
3.3 Using Discourse Context for Noisy Input . ................ 74
3.3.1 The Task: Discourse-sensitive Hypothesis Verification . - 75
3.3.2 The Data: Collection Annotation....................... 75
3.3.3 The Algorithm: Scoring curn Discourse .................. 76
3.3.4 The Results. Discourse-sensitive Hypothesis Verification 77
3.3.5 Roadmap.....................................- - - ■ . 83
3.4 Using Domain Context for Semantic Ambiguity .............. 83
3.4.1 The Task: Word Sense Disambiguation .................... 84
3-4.2 The Data: Collection Annotation . . ..... ..... 86
3.4.3 The Algorithm.; Scoring Word-Sense Ambiguities........... 87
3.4 4 The Results: Word Sense Disambiguation......... 90
3.5 Using Domain Context for Relation Extraction ......... 92
3-5-1 The Task: Relation Extraction............................ 93
3-5.2 The Data: Collection Annotation ............ 93
3.5.3 The Results; Relation Extraction 96
3.6 Evaluating Domain Context .................................. 97
3.6 1 The Task: Evaluating Ontological Fitness ................ 98
3.6.2 The Data: An Evaluation Suite ......................... 103
3.6.3 The Results: Ontological Fitness ....................... 106
3.7 Summing-up ................- - -.......................... . 109
3- 7-1 Roadmap.................... . ....................112
4 User and Situation 113
4.1 Modeling User and Situation ....................................114
4.1.1 Modeling the User.......................................114.
4.1.2 Modeling the Situation ..................................119
4.1.3 Pragmatics in SmartKom...................................120
4.1.4 Modeling Implicit Information ...........................124
4.1.5 Roadmap..................................................127
4.2 Using Situational Context for Underspecification..........128
4.2.1 The Task: Pragmatic Disambiguation ......................129
4.2.2 The Data: Collection Annotation ............ 130
4.2.3 The Algorithm: Scoring Construals .............. ... 136
4.2.4 The Results: Pragmatic Ambiguity ............. 137
4-3 Modeling What Matters 139
4,3T Ontological Choices Patterns......... ..... . . _ . 140
4.3.2 Foundational and Ground Knowledge.................. .. 141
4.3-3 Logical- and Content Patterns .......................... 142
4.4 Pragmatic Patterns . -........... 146
4.4.1 Implementing Pragmatic Patterns ............... „ . 147
4.4.2 Applying Pragmatic Patterns ................... . . . , 149
4- 4.3 Experimental Results; Decent ext ualization............ 156
CONTENTS
5
5 Oonclusion 161
5.1 Aims and Contributions......................................162
5.2 Future Work .............................................. 163
5.3 Concluding Remarks .........................................166
|
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| spelling | Porzel, Robert Verfasser (DE-588)140668853 aut Contextual computing for natural language processing von Robert Porzel 2009 200 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Bremen, Univ., Diss., 2009 Natürliche Sprache (DE-588)4041354-8 gnd rswk-swf Sprachverarbeitung (DE-588)4116579-2 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Natürliche Sprache (DE-588)4041354-8 s Sprachverarbeitung (DE-588)4116579-2 s DE-604 DE-601 pdf/application http://www.gbv.de/dms/tib-ub-hannover/616922787.pdf Inhaltsverzeichnis Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028848541&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028848541&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Porzel, Robert Contextual computing for natural language processing Natürliche Sprache (DE-588)4041354-8 gnd Sprachverarbeitung (DE-588)4116579-2 gnd |
| subject_GND | (DE-588)4041354-8 (DE-588)4116579-2 (DE-588)4113937-9 |
| title | Contextual computing for natural language processing |
| title_auth | Contextual computing for natural language processing |
| title_exact_search | Contextual computing for natural language processing |
| title_full | Contextual computing for natural language processing von Robert Porzel |
| title_fullStr | Contextual computing for natural language processing von Robert Porzel |
| title_full_unstemmed | Contextual computing for natural language processing von Robert Porzel |
| title_short | Contextual computing for natural language processing |
| title_sort | contextual computing for natural language processing |
| topic | Natürliche Sprache (DE-588)4041354-8 gnd Sprachverarbeitung (DE-588)4116579-2 gnd |
| topic_facet | Natürliche Sprache Sprachverarbeitung Hochschulschrift |
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