Recognizing textual entailment: models and applications
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
[San Rafael, Calif.]
Morgan & Claypool Publ.
2013
|
| Schriftenreihe: | Synthesis lectures on human language technologies
23 |
| Schlagwörter: | |
| Links: | http://www.gbv.de/dms/tib-ub-hannover/771760213.pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029815123&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Beschreibung: | Includes bibliographical references (p. 171-197) |
| Umfang: | XX, 200 Seiten graph. Darst. 24 cm |
| ISBN: | 1598298348 9781598298345 |
Internformat
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Datensatz im Suchindex
| _version_ | 1819296812860702720 |
|---|---|
| adam_text | Contents
List of Figures...................................................... xiii
List of Tables...........................................................XV
Preface................................................................xvii
Acknowledgments.........................................................xix
1 Textual Entailment........................................................1
1.1 Motivation and Rationale ...........................................1
1.2 The Recognizing Textual Entailment Task............................3
1.2.1 The Scope of Textual Entailment...............................3
1.2.2 The Role of Background Knowledge..............................6
1.2.3 Textual Entailment versus Linguistic Notion of Entailment.....7
1.2.4 Extending Entailment Recognition with Contradiction Detection.8
1.2.5 The Challenge and Opportunity of RTE..........................9
1.3 Applications of Textual Entailment Solutions..................... 10
1.3.1 Question Answering...........................................10
1.3.2 Relation Extraction .................. . .. v................12
1.3.3 Text Summarization.........................;.................13
1.3.4 Additional Applications..................................... 14
1.4 Textual Entailment Evaluation......................................16
1.4.1 RTE-1 through RTE-5..........................................17
1.4.2 RTE-6 and RTE-7..............................................19
1.4.3 Other Evaluations of Textual Entailment Technology...........21
1.4.4 Future Directions for Entailment Evaluation..................22
2 Architectures and Approaches........................................... 25
2.1 An Intuitive Model for RTE.........................................25
2.2 Levels of Representation in RTE Systems ...........................27
2.2.1 Lexical-level RTE............................................28
2.2.2 Structured Representations for RTE......................... 29
X
2.3 Inference in RTE systems.................................................33
2.3.1 Similarity-based approaches.......................................34
2.3.2 Alignment-focused Approaches......................................35
2.3.3 “Proof Theoretic” RTE.............................................35
2.3.4 Hybrid Approaches.................................................43
2.4 A conceptual architecture for RTE systems ...............................43
2.4.1 Preprocessing.....................................................44
2.4.2 Enrichment........................................................44
2.4.3 Candidate Alignment Generation....................................46
2.4.4 Alignment Selection...............................................46
2.4.5 Classification....................................................47
2.4.6 Main Decision-making Approaches ..................................47
2.5 Emergent Challenges......................................................47
2.5.1 Knowledge Acquisition Bottleneck Acquiring Rules..................48
2.5.2 Noise-tolerant RTE architectures..................................48
3 Alignment, Classification, and Learning........................................51
3.1 An Abstract Scheme for Textual Entailment Decisions......................52
3.2 Generating Canditates and Selecting Alignments ..........................54
3.2.1 Anchors: Linking Texts and Hypotheses.............................54
3.2.2 Formalizing Candidate Alignment Generation and Alignment..........56
3.3 Classifiers, Feature Spaces, and Machine Learning........................58
3.4 Similarity feature spaces................................................59
3.4.1 Token-level Similarity Features ..................................59
3.4.2 Structured Similarity Features ...................................61
3.4.3 Entailment Trigger Feature Spaces.................................67
3.4.4 Rewrite Rule Feature Spaces.......................................69
3.4.5 Discussion........................................................75
3.5 Learning Alignment Functions.............................................76
3.5.1 Learning Alignment from Gold-standard Data........................76
3.5.2 Learning Entailment with a Latent Alignment.......................78
4 Case Studies ..................................................................81
4.1 Edit Distance-based RTE..................................................81
4.1.1 Open Source Tree Edit-based RTE System ...........................82
4.1.2 Tree Edit Distance with Expanded Edit Types.......................82
4.2 Logical Representation and Inference.....................................83
4.2.1 Representation...................................................84
4.2.2 Logical Inference with Abduction...............................86
4.2.3 Logical Inference with Shallow Backoff System..................88
4.3 Transformation-based Approaches.........................................90
4.3.1 Transformation-based Approach with Integer Linear Programming ... 90
4.3.2 Syntactic Transformation with linguistically motivated rules.....92
4.3.3 Syntactic Transformation with a Probabilistic Calculus...........94
4.3.4 Syntactic Transformation with Learned Operation Costs............96
4.3.5 Natural Logic....................................................96
4.4 Alignment-focused Approaches......................................... 100
4.4.1 Learning Alignment Selection Independently of Entailment........100
4.4.2 Hand-Coded Alignment Function...................................104
4.4.3 Leveraging Multiple Alignments for RTE.........................105
4.4.4 Aligning Discourse Commitments..................................107
4.4.5 Latent Alignment Inference for RTE.............................110
4.5 Paired Similarity Approaches...........................................112
4.6 Ensemble Systems..................................................... 115
4.6.1 Weighted Expert Approach........................................115
4.6.2 Selective Expert Approach.......................................117
4.7 Discussion........................................................... 119
5 Knowledge Acquisition for Textual Entailment................................123
5.1 Scope of Target Knowledge.......................................... 123
5.2 Acquisition from Manually Constructed Knowledge Resources .............125
5.2.1 Mining computation-oriented knowledge resources.................125
5.2.2 Mining human-oriented knowledge resources.......................129
5.3 Corpus-based Knowledge Acquisition................................... 130
5.3.1 Distributional Similarity Methods...............................131
5.3.2 Co-occurrence-based Methods.....................................139
5.3.3 Acquisition from Parallel and Comparable Corpora...............142
5.4 Integrating Multiple Sources of Evidence...............................144
5.4.1 Integrating Multiple Information Sources .......................144
5.4.2 Simultaneous Global Learning of Multiple Rules..................146
5.5 Context Sensitivity of Entailment Rules................................148
5.6 Concluding Remarks and Future Directions...............................151
XU
6 Research Directions in RTE..................................................157
6.1 Development of better/more flexible preprocessing tool chain........158
6.2 Knowledge Acquisition and Specification..............................159
6.3 Open Source Platform for Textual Entailment..........................160
6.4 Task Elaboration and Phenomenon-specific RTE Resources...............161
6.5 Learning and Inference: efficient, scalable algorithms ............ 162
6.6 Conclusion...........................................................163
A Entailment Phenomena........................................................165
Bibliography...............................................................171
Authors’Biographies .......................................................199
|
| any_adam_object | 1 |
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| author_facet | Dagan, ʿIdo 1977- |
| author_role | aut |
| author_sort | Dagan, ʿIdo 1977- |
| author_variant | ʿ d ʿd |
| building | Verbundindex |
| bvnumber | BV044413372 |
| classification_rvk | ST 306 |
| ctrlnum | (OCoLC)903620083 (DE-599)GBV771760213 |
| discipline | Informatik |
| format | Book |
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| id | DE-604.BV044413372 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T18:02:25Z |
| institution | BVB |
| isbn | 1598298348 9781598298345 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029815123 |
| oclc_num | 903620083 |
| open_access_boolean | |
| owner | DE-739 |
| owner_facet | DE-739 |
| physical | XX, 200 Seiten graph. Darst. 24 cm |
| publishDate | 2013 |
| publishDateSearch | 2013 |
| publishDateSort | 2013 |
| publisher | Morgan & Claypool Publ. |
| record_format | marc |
| series | Synthesis lectures on human language technologies |
| series2 | Synthesis lectures on human language technologies |
| spellingShingle | Dagan, ʿIdo 1977- Recognizing textual entailment models and applications Synthesis lectures on human language technologies Natural language processing (Computer science)xMethodology Semantic computing Textverarbeitung (DE-588)4059667-9 gnd Folgerung (DE-588)4267223-5 gnd Sprachverarbeitung (DE-588)4116579-2 gnd |
| subject_GND | (DE-588)4059667-9 (DE-588)4267223-5 (DE-588)4116579-2 |
| title | Recognizing textual entailment models and applications |
| title_auth | Recognizing textual entailment models and applications |
| title_exact_search | Recognizing textual entailment models and applications |
| title_full | Recognizing textual entailment models and applications Ido Dagan ... [et al.] |
| title_fullStr | Recognizing textual entailment models and applications Ido Dagan ... [et al.] |
| title_full_unstemmed | Recognizing textual entailment models and applications Ido Dagan ... [et al.] |
| title_short | Recognizing textual entailment |
| title_sort | recognizing textual entailment models and applications |
| title_sub | models and applications |
| topic | Natural language processing (Computer science)xMethodology Semantic computing Textverarbeitung (DE-588)4059667-9 gnd Folgerung (DE-588)4267223-5 gnd Sprachverarbeitung (DE-588)4116579-2 gnd |
| topic_facet | Natural language processing (Computer science)xMethodology Semantic computing Textverarbeitung Folgerung Sprachverarbeitung |
| url | http://www.gbv.de/dms/tib-ub-hannover/771760213.pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029815123&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV035447238 |
| work_keys_str_mv | AT daganʿido recognizingtextualentailmentmodelsandapplications |