Semantic Parsing using Distributional Semantics and Probabilistic Logic

UTexas: Natural Language Semantics using Distributional Semantics and Probabilistic Logic

Representing Meaning with a Combination of Logical and Distributional Models

NLP tasks differ in the semantic information they require, and at this time no single se- mantic representation fulfills all requirements. Logic-based representations characterize sentence structure, but do not capture t…

DisCoCat for Donkey Sentences

We demonstrate how to parse Geach's Donkey sentences in a compositional distributional model of meaning. We build on previous work on the DisCoCat (Distributional Compositional Categorical) framework, including extension…

Declarative Probabilistic Logic Programming in Discrete-Continuous Domains

Over the past three decades, the logic programming paradigm has been successfully expanded to support probabilistic modeling, inference and learning. The resulting paradigm of probabilistic logic programming (PLP) and it…

Functional Distributional Semantics

Vector space models have become popular in distributional semantics, despite the challenges they face in capturing various semantic phenomena. We propose a novel probabilistic framework which draws on both formal semanti…