Category Textual Entailment
Modeling Extractive Sentence Intersection via Subtree Entailment. Omer Levy, Ido Dagan, Gabriel Stanovsky, Judith Eckle-Kohler, and Iryna Gurevych. COLING 2016. [pdf] Sentence intersection captures the semantic overlap of two texts, generalizing over paradigms such as textual entailment and semantic text similarity. Despite its modeling power, it has received little attention because it is difficult for […]
Learning to Exploit Structured Resources for Lexical Inference. Vered Shwartz, Omer Levy, Ido Dagan and Jacob Goldberger. CoNLL 2015. [pdf] [supplementary] This paper presents a supervised framework for automatically selecting an optimized subset of resource relations for a given target inference task. Our approach enables the use of large-scale knowledge resources, thus providing a rich […]
Proposition Knowledge Graphs. Gabriel Stanovsky, Omer Levy, and Ido Dagan. AHA! Workshop 2014. [pdf] This position paper proposes a novel representation for Information Discovery — Proposition Knowledge Graphs. These extend the Open IE paradigm by representing semantic inter-proposition relations in a traversable graph. . . . . .
Focused Entailment Graphs for Open IE Propositions. Omer Levy, Ido Dagan, and Jacob Goldberger. CoNLL 2014. [pdf] [slides] 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 […]
The Excitement Open Platform for Textual Inferences. Bernardo Magnini, Roberto Zanoli, Ido Dagan, Kathrin Eichler, Günter Neumann, Tae-Gil Noh, Sebastian Padó, Asher Stern, and Omer Levy. Demo paper in ACL 2014. [pdf] This paper presents the Excitement Open Platform (EOP), a generic architecture and a comprehensive implementation for textual inference in multiple languages. Code The […]
Recognizing Partial Textual Entailment. Omer Levy, Torsten Zesch, Ido Dagan, and Iryna Gurevych. Short paper in ACL 2013. [pdf] [slides] Textual entailment is an asymmetric relation between two text fragments that describes whether one fragment can be inferred from the other. It thus cannot capture the notion that the target fragment is “almost entailed” by the […]
UKP-BIU: Similarity and Entailment Metrics for Student Response Analysis. Torsten Zesch, Omer Levy, Iryna Gurevych, and Ido Dagan. SemEval 2013. [pdf] [slides] Given a question, a reference answer, and a student’s answer, the task is to determine whether the student answered correctly. While this is not a new task in itself, the challenge focuses on employing textual entailment technologies […]