Foundations of statistical natural language processing
Foundations of statistical natural language processing
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
The syntactic process
Computational Linguistics
Learning to paraphrase: an unsupervised approach using multiple-sequence alignment
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
The design and implementation of VAMPIRE
AI Communications - CASC
Parsing the WSJ using CCG and log-linear models
ACL '04 Proceedings of the 42nd Annual Meeting on 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 learning and abductive reasoning
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
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
Textual entailment recognition based on dependency analysis and wordnet
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Without a 'doubt'?: unsupervised discovery of downward-entailing operators
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Textual entailment as an evaluation framework for metaphor resolution: a proposal
STEP '08 Proceedings of the 2008 Conference on Semantics in Text Processing
Natural language as the basis for meaning representation and inference
CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing
Translating between language and logic: what is easy and what is difficult
CADE'11 Proceedings of the 23rd international conference on Automated deduction
On the automatic generation of intermediate logic forms for wordnet glosses
CICLing'10 Proceedings of the 11th international conference on Computational Linguistics and Intelligent Text Processing
Entailment above the word level in distributional semantics
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Leveraging Diverse Lexical Resources for Textual Entailment Recognition
ACM Transactions on Asian Language Information Processing (TALIP) - Special Issue on RITE
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We use logical inference techniques for recognising textual entailment, with theorem proving operating on deep semantic interpretations as the backbone of our system. However, the performance of theorem proving on its own turns out to be highly dependent on a wide range of background knowledge, which is not necessarily included in publically available knowledge sources. Therefore, we achieve robustness via two extensions. Firstly, we incorporate model building, a technique borrowed from automated reasoning, and show that it is a useful robust method to approximate entailment. Secondly, we use machine learning to combine these deep semantic analysis techniques with simple shallow word overlap. The resulting hybrid model achieves high accuracy on the RTE testset, given the state of the art. Our results also show that the various techniques that we employ perform very differently on some of the subsets of the RTE corpus and as a result, it is useful to use the nature of the dataset as a feature.