Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
A categorial variation database for English
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
A noisy-channel approach to question answering
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Question answering passage retrieval using dependency relations
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Robust textual inference via graph matching
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Yago: a core of semantic knowledge
Proceedings of the 16th international conference on World Wide Web
Scaling textual inference to the web
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Robust textual inference via learning and abductive reasoning
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Tree edit models for recognizing textual entailments, paraphrases, and answers to questions
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
A probabilistic modeling framework for lexical entailment
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Towards a probabilistic model for lexical entailment
TIWTE '11 Proceedings of the TextInfer 2011 Workshop on Textual Entailment
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Identifying textual inferences, where the meaning of one text follows from another, is a general underlying task within many natural language applications. Commonly, it is approached either by generative syntactic-based methods or by "lightweight" heuristic lexical models. We suggest a model which is confined to simple lexical information, but is formulated as a principled generative probabilistic model. We focus our attention on the task of ranking textual inferences and show substantially improved results on a recently investigated question answering data set.