Abstract
Open IE methods extract structured propositions from text. However, these propositions are neither consolidated nor generalized, and querying them may lead to insufficient or redundant information. This work suggests an approach to organize open IE propositions using entailment graphs. The entailment relation unifies equivalent propositions and induces a specific-to-general structure. We create a large dataset of gold-standard proposition entailment graphs, and provide a novel algorithm for automatically constructing them. Our analysis shows that predicate entailment is extremely context-sensitive, and that current lexical-semantic resources do not capture many of the lexical inferences induced by proposition entailment.
| Original language | English |
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| Title of host publication | CoNLL 2014 - 18th Conference on Computational Natural Language Learning, Proceedings |
| Publisher | Association for Computational Linguistics (ACL) |
| Pages | 87-97 |
| Number of pages | 11 |
| ISBN (Electronic) | 9781941643020 |
| DOIs | |
| State | Published - 2014 |
| Event | 18th Conference on Computational Natural Language Learning, CoNLL 2014 - Baltimore, United States Duration: 26 Jun 2014 → 27 Jun 2014 |
Publication series
| Name | CoNLL 2014 - 18th Conference on Computational Natural Language Learning, Proceedings |
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Conference
| Conference | 18th Conference on Computational Natural Language Learning, CoNLL 2014 |
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| Country/Territory | United States |
| City | Baltimore |
| Period | 26/06/14 → 27/06/14 |
Bibliographical note
Publisher Copyright:© 2014 Association for Computational Linguistics.