Representing Meaning with a Combination of Logical and Distributional Models

TL;DR

This paper presents a hybrid semantic representation combining logical and distributional models using probabilistic logic in MLNs, applied to textual entailment with state-of-the-art results.

Contribution

It introduces a practical system integrating logical and distributional semantics via probabilistic inference, with novel methods for knowledge base construction and resource integration.

Findings

Achieved state-of-the-art results on the SICK dataset for textual entailment.

Demonstrated effective integration of distributional and logical semantic sources.

Provided efficient probabilistic inference methods for hybrid semantic models.

Abstract

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 the graded aspect of meaning. Distributional models give graded similarity ratings for words and phrases, but do not capture sentence structure in the same detail as logic-based approaches. So it has been argued that the two are complementary. We adopt a hybrid approach that combines logic-based and distributional semantics through probabilistic logic inference in Markov Logic Networks (MLNs). In this paper, we focus on the three components of a practical system integrating logical and distributional models: 1) Parsing and task representation is the logic-based part where input problems are represented in probabilistic logic. This is quite different from representing them in standard first-order logic. 2) For knowledge base construction we form weighted inference rules. We integrate and compare distributional information with other sources, notably WordNet and an existing paraphrase collection. In particular, we use our system to evaluate distributional lexical entailment approaches. We use a variant of Robinson resolution to determine the necessary inference rules. More sources can easily be added by mapping them to logical rules; our system learns a resource-specific weight that corrects for scaling differences between resources. 3) In discussing probabilistic inference, we show how to solve the inference problems efficiently. To evaluate our approach, we use the task of textual entailment (RTE), which can utilize the strengths of both logic-based and distributional representations. In particular we focus on the SICK dataset, where we achieve state-of-the-art results.