| Description |
1 online resource. |
| Series |
Synthesis lectures on human language technologies ; #23. 1947-4040
|
| Reproduction |
Electronic reproduction. Perth, W.A. Available via World Wide Web. |
| Note |
Description based on online resource; title from PDF t.p. (Morgan & Claypool, viewed on August 14, 2013). |
| Bibliography |
Includes bibliographical references (p. 171-197). |
| Contents |
1. Textual entailment -- 1.1 Motivation and rationale -- 1.2 The recognizing textual entailment task -- 1.2.1 The scope of textual entailment -- 1.2.2 The role of background knowledge -- 1.2.3 Textual entailment versus linguistic notion of entailment -- 1.2.4 Extending entailment recognition with contradiction detection -- 1.2.5 The challenge and opportunity of RTE -- 1.3 Applications of textual entailment solutions -- 1.3.1 Question answering -- 1.3.2 Relation extraction -- 1.3.3 Text summarization -- 1.3.4 Additional applications -- 1.4 Textual entailment evaluation -- 1.4.1 RTE-1 through RTE-5 -- 1.4.2 RTE-6 and RTE-7 -- 1.4.3 Other evaluations of textual entailment technology -- 1.4.4 Future directions for entailment evaluation -- |
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2. Architectures and approaches -- 2.1 An intuitive model for RTE -- 2.2 Levels of representation in RTE systems -- 2.2.1 Lexical-level RTE -- 2.2.2 Structured representations for RTE -- 2.3 Inference in RTE systems -- 2.3.1 Similarity-based approaches -- 2.3.2 Alignment-focused approaches -- 2.3.3 "Proof Theoretic" RTE -- 2.3.4 Hybrid approaches -- 2.4 A conceptual architecture for RTE systems -- 2.4.1 Preprocessing -- 2.4.2 Enrichment -- 2.4.3 Candidate alignment generation -- 2.4.4 Alignment selection -- 2.4.5 Classification -- 2.4.6 Main decision-making approaches -- 2.5 Emergent challenges -- 2.5.1 Knowledge acquisition bottleneck: acquiring rules -- 2.5.2 Noise-tolerant RTE architectures -- |
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3. Alignment, classification, and learning -- 3.1 An abstract scheme for textual entailment decisions -- 3.2 Generating candidates and selecting alignments -- 3.2.1 Anchors: linking texts and hypotheses -- 3.2.2 Formalizing candidate alignment generation and alignment -- 3.3 Classifiers, feature spaces, and machine learning -- 3.4 Similarity feature spaces -- 3.4.1 Token-level similarity features -- 3.4.2 Structured similarity features -- 3.4.3 Entailment trigger feature spaces -- 3.4.4 Rewrite rule feature spaces -- 3.4.5 Discussion -- 3.5 Learning alignment functions -- 3.5.1 Learning alignment from gold-standard data -- 3.5.2 Learning entailment with a latent alignment -- |
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4. Case studies -- 4.1 Edit distance-based RTE -- 4.1.1 Open source tree edit-based RTE system -- 4.1.2 Tree edit distance with expanded edit types -- 4.2 Logical representation and inference -- 4.2.1 Representation -- 4.2.2 Logical inference with abduction -- 4.2.3 Logical inference with shallow backoff system -- 4.3 Transformation-based approaches -- 4.3.1 Transformation-based approach with integer linear programming -- 4.3.2 Syntactic transformation with linguistically motivated rules -- 4.3.3 Syntactic transformation with a probabilistic calculus -- 4.3.4 Syntactic transformation with learned operation costs -- 4.3.5 Natural logic -- 4.4 Alignment-focused approaches -- 4.4.1 Learning alignment selection independently of entailment -- 4.4.2 Hand-coded alignment function -- 4.4.3 Leveraging multiple alignments for RTE -- 4.4.4 Aligning discourse commitments -- 4.4.5 Latent alignment inference for RTE -- 4.5 Paired similarity approaches -- 4.6 Ensemble systems -- 4.6.1 Weighted expert approach -- 4.6.2 Selective expert approach -- 4.7 Discussion -- |
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5. Knowledge acquisition for textual entailment -- 5.1 Scope of target knowledge -- 5.2 Acquisition from manually constructed knowledge resources -- 5.2.1 Mining computation-oriented knowledge resources -- 5.2.2 Mining human-oriented knowledge resources -- 5.3 Corpus-based knowledge acquisition -- 5.3.1 Distributional similarity methods -- 5.3.2 Co-occurrence-based methods -- 5.3.3 Acquisition from parallel and comparable corpora -- 5.4 Integrating multiple sources of evidence -- 5.4.1 Integrating multiple information sources -- 5.4.2 Simultaneous global learning of multiple rules -- 5.5 Context sensitivity of entailment rules -- 5.6 Concluding remarks and future directions -- |
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6. Research directions in RTE -- 6.1 Development of better/more flexible preprocessing tool chain -- 6.2 Knowledge acquisition and specification -- 6.3 Open source platform for textual entailment -- 6.4 Task elaboration and phenomenon-specific RTE resources -- 6.5 Learning and inference: efficient, scalable algorithms -- 6.6 Conclusion -- |
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A. Entailment phenomena -- Bibliography -- Authors' biographies. |
| Subject |
Natural language processing (Computer science)
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Entailment (Logic) -- Computer programs.
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Knowledge acquisition (Expert systems)
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| Added Author |
Dagan, Ido.
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|
Ebooks Corporation
|
| Related To |
Print version: Dagan, Ido. Recognizing textual entailment. San Rafael : Morgan & Claypool, 2009 1598298348 |
| ISBN |
9781598298352 (electronic bk.) |
|
1598298356 (electronic bk.) |
|
9781598298345 (pbk.) |
| UPC # |
10.2200/S00509ED1V01Y201305HLT023 doi |
| OCLC # |
EBC919788 |
|
