Discovery of inference rules for question-answering
Natural Language Engineering
Findex: search result categories help users when document ranking fails
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Model-driven formative evaluation of exploratory search: A study under a sensemaking framework
Information Processing and Management: an International Journal
The cluster-abstraction model: unsupervised learning of topic hierarchies from text data
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Open information extraction from the web
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Global learning of focused entailment graphs
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Identifying relations for open information extraction
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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We present a novel text exploration model, which extends the scope of state-of-the-art technologies by moving from standard concept-based exploration to statement-based exploration. The proposed scheme utilizes the textual entailment relation between statements as the basis of the exploration process. A user of our system can explore the result space of a query by drilling down/up from one statement to another, according to entailment relations specified by an entailment graph and an optional concept taxonomy. As a prominent use case, we apply our exploration system and illustrate its benefit on the health-care domain. To the best of our knowledge this is the first implementation of an exploration system at the statement level that is based on the textual entailment relation.