Semantic Inference at the Lexical-Syntactic Level for Textual Entailment Recognition
By Roy Bar-Haim, Ido Dagan, Iddo Greental, Idan Szpektor, Moshe Friedman. 2007
41 citations recorded by the source corpus.
We present a new framework for textual entailment, which provides a modular integration between knowledge-based exact inference and cost-based approximate matching. Diverse types of knowledge are uniformly represented as entailment rules, which were acquired both manually and automatically. Our proof system operates directly on parse trees, and infers new trees by applying entailment rules, aiming to strictly generate the target hypothesis from the source text. In order to cope with inevitable knowledge gaps, a cost function is used to measure the remaining "distance" from the hypothesis.
Subfields: Artificial Intelligence, Natural Language Processing
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
- Andreas Vlachos
- Sebastian Riedel
- Ido Dagan
- Amrith Krishna
- Asher Stern
- Mohit Bansal
- Yoad Winter
- Assaf Toledo
In Natural Language Processing
- Andreas Vlachos
- Sebastian Riedel
- Ido Dagan
- Asher Stern
- Amrith Krishna
- Yoad Winter
- Sophia Katrenko
- Stavroula Alexandropoulou
Citations in the corpus
- ProoFVer: Natural Logic Theorem Proving for Fact Verification (2021, methodology): “Similar proof systems have used mutations in the form of tree-edit operations (Mehdad, 2009), transformations over syntactic parses (Heilman and Smith, 2010; Harmeling, 2009), knowledge-based transformations in the form of lexical mutations, entailment rules, rewrite rules, or their combinations (Ba”
- A Confidence Model for Syntactically-Motivated Entailment Proofs (2011, methodology): “We utilize knowledge-based sub-tree transformations, following (Bar-Haim et al., 2007a), but augment them with a set of on the fly transformations that correspond to syntactically-motivated entailment inferences, whose reliability can be learned using syntactic features.”
- Semantic Annotation for Textual Entailment Recognition (2012, methodology): “Following the design first introduced in [3], BIUTEE employs a syntactic rule base comprised of hand-crafted rules [22] that represent syntactic transformations.”