Learning Entailment Relations by Global Graph Structure Optimization
By Jonathan Berant, Ido Dagan, Jacob Goldberger. 2012
46 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Natural Language Processing, Graph Learning
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Mohit Bansal
- Julia Hockenmaier
- Jianfeng Gao
- Omer Levy
- Ido Dagan
- Luke S. Zettlemoyer
- Micah Hodosh
- Yejin Choi
In Natural Language Processing
- Mohit Bansal
- Julia Hockenmaier
- Jianfeng Gao
- Micah Hodosh
- Yejin Choi
- Christopher D. Manning
- Samuel R. Bowman
- Omer Levy
In Graph Learning
Citations in the corpus
- From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions (2014, background): “The de-notation graph is similar to Berant et al. (2012)’s ‘entailment graph’, but differs from it in two ways: first, entailment relations in the denotation graph are defined extensionally in terms of the images described by the expressions at each node, and second, nodes in Berant et al.’s…”
- Align, Disambiguate and Walk: A Unified Approach for Measuring Semantic Similarity (2013, unknown): “Semantic similarity is a core technique for many topics in Natural Language Processing such as Textual Entailment (Berant et al., 2012), Semantic Role Labeling (F¨urstenau and Lapata, 2012), and Question Answering (Surdeanu et al., 2011).”
- Focused Entailment Graphs for Open IE Propositions (2014, background): “The same phenomenon was also observed in the work on predicate entailment graphs (Berant et al., 2012”