Readings in natural language processing
From English to logic: context-free computation of “conventional” logical translations
Readings in natural language processing
Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
DIRT @SBT@discovery of inference rules from text
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
ACL '88 Proceedings of the 26th annual meeting on Association for Computational Linguistics
COGEX: a logic prover for question answering
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Integer linear programming inference for conditional random fields
ICML '05 Proceedings of the 22nd international conference on Machine learning
Towards light semantic processing for Question Answering
HLT-NAACL-TEXTMEANING '03 Proceedings of the HLT-NAACL 2003 workshop on Text meaning - Volume 9
The Description Logic Handbook
The Description Logic Handbook
Semantic role labeling via integer linear programming inference
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Identification and tracing of ambiguous names: discriminative and generative approaches
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
A probabilistic classification approach for lexical textual entailment
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Robust textual inference via learning and abductive reasoning
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
The necessity of syntactic parsing for semantic role labeling
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Learning with feature description logics
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
A semantic approach to recognizing textual entailment
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
SeseiOnto: Interfacing NLP and Ontology Extraction
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Identifying semantic equivalence for multi-document summarisation
Artificial Intelligence Review
Employing a Domain Specific Ontology to Perform Semantic Search
ICCS '08 Proceedings of the 16th international conference on Conceptual Structures: Knowledge Visualization and Reasoning
A machine learning approach to textual entailment recognition
Natural Language Engineering
Recognizing textual entailment using sentence similarity based on dependency tree skeletons
RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
Dependency-based paraphrasing for recognizing textual entailment
RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
Textual entailment recognition using a linguistically–motivated decision tree classifier
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Applying COGEX to recognize textual entailment
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
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Semantic entailment is the problem of determining if the meaning of a given sentence entails that of another. We present a principled approach to semantic entailment that builds on inducing re-representations of text snippets into a hierarchical knowledge representation along with an optimization-based inferential mechanism that makes use of it to prove semantic entailment. This paper provides details and analysis of the knowledge representation and knowledge resources issues encountered. We analyze our system's behavior on the PASCAL text collection and the PARC collection of question-answer pairs. This is used to motivate and explain some of the design decisions in our hierarchical knowledge representation, that is centered around a predicate-argument type abstract representation of text.