Learning to Predict Denotational Probabilities For Modeling Entailment
نویسندگان
چکیده
We propose a framework that captures the denotational probabilities of words and phrases by embedding them in a vector space, and present a method to induce such an embedding from a dataset of denotational probabilities. We show that our model successfully predicts denotational probabilities for unseen phrases, and that its predictions are useful for textual entailment datasets such as SICK and SNLI.
منابع مشابه
A Probabilistic Setting And Lexical Coocurrence Model For Textual Entailment
This paper proposes a general probabilistic setting that formalizes a probabilistic notion of textual entailment. We further describe a particular preliminary model for lexical-level entailment, based on document cooccurrence probabilities, which follows the general setting. The model was evaluated on two application independent datasets, suggesting the relevance of such probabilistic approache...
متن کاملA Probabilistic Setting and Lexical Cooccurrence Model for Textual Entailment
This paper proposes a general probabilistic setting that formalizes a probabilistic notion of textual entailment. We further describe a particular preliminary model for lexical-level entailment, based on document cooccurrence probabilities, which follows the general setting. The model was evaluated on two application independent datasets, suggesting the relevance of such probabilistic approache...
متن کاملChinese Textual Entailment Recognition Based on Syntactic Tree Clipping
Textual entailment has been proposed as a unifying generic framework for modeling language variability and semantic inference in different Natural Language Processing (NLP) tasks. This paper presents a novel statistical method for recognizing Chinese textual entailment in which lexical, syntactic with semantic matching features are combined together. In order to solve the problems of syntactic ...
متن کاملA Vector Space for Distributional Semantics for Entailment
Distributional semantics creates vectorspace representations that capture many forms of semantic similarity, but their relation to semantic entailment has been less clear. We propose a vector-space model which provides a formal foundation for a distributional semantics of entailment. Using a mean-field approximation, we develop approximate inference procedures and entailment operators over vect...
متن کاملA denotational semantics for equilibrium logic
In this paper we provide an alternative semantics for Equilibrium Logic and its monotonic basis, the logic of Here-and-There (also known as Gödel’s G3 logic) that relies on the idea of denotation of a formula, that is, a function that collects the set of models of that formula. Using the threevalued logic G3 as a starting point and an ordering relation (for which equilibrium/stable models are m...
متن کامل