Logical foundations of artificial intelligence
Logical foundations of artificial intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
WordNet: a lexical database for English
Communications of the ACM
Communications of the ACM
Learning in graphical models
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
ACL '85 Proceedings of the 23rd annual meeting on Association for Computational Linguistics
Lexical chains for question answering
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Logic form transformation of WordNet and its applicability to question answering
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Machine Learning
Annotating the propositions in the Penn Chinese Treebank
SIGHAN '03 Proceedings of the second SIGHAN workshop on Chinese language processing - Volume 17
The Proposition Bank: An Annotated Corpus of Semantic Roles
Computational Linguistics
Hownet And the Computation of Meaning
Hownet And the Computation of Meaning
Methods for using textual entailment in open-domain question answering
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Wide-coverage semantic representations from a CCG parser
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Robust textual inference via graph matching
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Recognising textual entailment with logical inference
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Measuring the semantic similarity of texts
EMSEE '05 Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment
The third PASCAL recognizing textual entailment challenge
RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
Robust machine translation evaluation with entailment features
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Markov Logic: An Interface Layer for Artificial Intelligence
Markov Logic: An Interface Layer for Artificial Intelligence
A survey of paraphrasing and textual entailment methods
Journal of Artificial Intelligence Research
Tuffy: scaling up statistical inference in Markov logic networks using an RDBMS
Proceedings of the VLDB Endowment
Integrating logical representations with probabilistic information using Markov logic
IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics
The PASCAL recognising textual entailment challenge
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Lexical entailment for information retrieval
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
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Recognizing inference in texts (RITE) attracts growing attention of natural language processing (NLP) researchers in recent years. In this article, we propose a novel approach to recognize inference with probabilistic logical reasoning. Our approach is built on Markov logic networks (MLNs) framework, which is a probabilistic extension of first-order logic. We design specific semantic rules based on the surface, syntactic, and semantic representations of texts, and map these rules to logical representations. We also extract information from some knowledge bases as common sense logic rules. Then we utilize MLNs framework to make predictions with combining statistical and logical reasoning. Experiment results shows that our system can achieve better performance than state-of-the-art RITE systems.