Automated Reasoning with Otter
Automated Reasoning with Otter
Learning surface text patterns for a Question Answering system
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
The structure and performance of an open-domain question answering system
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Parsing and question classification for question answering
ODQA '01 Proceedings of the workshop on Open-domain question answering - Volume 12
Combining term-based and event-based matching for question answering
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Application of kalman filters to identify unexpected change in blogs
Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries
An inference model for semantic entailment in natural language
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
BRUJA: question classification for Spanish. Using machine translation and an English classifier
MLQA '06 Proceedings of the Workshop on Multilingual Question Answering
From generalization of syntactic parse trees to conceptual graphs
ICCS'10 Proceedings of the 18th international conference on Conceptual structures: from information to intelligence
Using generalization of syntactic parse trees for taxonomy capture on the web
ICCS'11 Proceedings of the 19th international conference on Conceptual structures for discovering knowledge
An inference model for semantic entailment in natural language
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Machine learning of syntactic parse trees for search and classification of text
Engineering Applications of Artificial Intelligence
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The paper presents a lightweight knowledge-based reasoning framework for the JAVELIN open-domain Question Answering (QA) system. We propose a constrained representation of text meaning, along with a flexible unification strategy that matches questions with retrieved passages based on semantic similarities and weighted relations between words.