# Efficient Markov Logic Inference for Natural Language Semantics

@inproceedings{Beltagy2014EfficientML, title={Efficient Markov Logic Inference for Natural Language Semantics}, author={Iz Beltagy and Raymond J. Mooney}, booktitle={StarAI@AAAI}, year={2014} }

Using Markov logic to integrate logical and distributional information in natural-language semantics results in complex inference problems involving long, complicated formulae. Current inference methods for Markov logic are ineffective on such problems. To address this problem, we propose a new inference algorithm based on SampleSearch that computes probabilities of complete formulae rather than ground atoms. We also introduce a modified closed-world assumption that significantly reduces the…

## 18 Citations

### Natural Language Semantics using Probabilistic Logic

- Computer Science
- 2014

This work proposes using probabilistic logic to represent natural language semantics combining the expressivity and the automated inference of logic, and the gradedness of distributional representations.

### UTexas: Natural Language Semantics using Distributional Semantics and Probabilistic Logic

- Computer Science*SEMEVAL
- 2014

This work represents natural language semantics by combining logical and distributional information in probabilistic logic in Markov Logic Networks and Probabilistic Soft Logic for the RTE and STS tasks.

### Representing Meaning with a Combination of Logical and Distributional Models

- Computer ScienceCL
- 2016

This article adopts a hybrid approach that combines logical and distributional semantics using probabilistic logic, specifically Markov Logic Networks and releases a lexical entailment data set of 10,213 rules extracted from the SICK data set, which is a valuable resource for evaluating lexical entailsment systems.

### Markov Logic Networks for Natural Language Question Answering

- Computer ScienceArXiv
- 2015

The experiments, demonstrating a 15\% accuracy boost and a 10x reduction in runtime, suggest that the flexibility and different inference semantics of Praline are a better fit for the natural language question answering task.

### On the Proper Treatment of Quantifiers in Probabilistic Logic Semantics

- Computer Science, PhilosophyIWCS
- 2015

This paper shows how to formulate RTE inference problems in probabilistic logic in a way that takes the domain closure and closed-world assumptions into account, and achieves 100% accuracy on the synthetic dataset and on the relevant part of FraCas.

### Representing Meaning with a Combination of Logical Form and Vectors

- Computer ScienceArXiv
- 2015

A hybrid approach that combines logic-based and distributional semantics through probabilistic logic inference in Markov Logic Networks (MLNs) is adopted, and a state-of-the-art result is achieved on the SICK dataset.

### Exploring Markov Logic Networks for Question Answering

- Computer ScienceEMNLP
- 2015

A system that reasons with knowledge derived from textbooks, represented in a subset of firstorder logic, called Praline, which demonstrates a 15% accuracy boost and a 10x reduction in runtime as compared to other MLNbased methods, and comparable accuracy to word-based baseline approaches.

### Tractable Probabilistic Reasoning Through Effective Grounding

- Computer Science
- 2019

This position paper will draw attention to open research areas around efficiently instantiating templated probabilistic models.

### Reasoning about Unmodelled Concepts - Incorporating Class Taxonomies in Probabilistic Relational Models

- Computer ScienceArXiv
- 2015

This work proposes fuzzy inference in Markov logic networks, which enables the use of taxonomic knowledge as a source of imposing structure onto possible worlds and shows that by exploiting this structure, probability distributions can be represented more compactly and that the reasoning systems become capable of reasoning about concepts not contained in the probabilistic knowledge base.

### Interpretation of Natural-language Robot Instructions: Probabilistic Knowledge Representation, Learning, and Reasoning

- Computer Science
- 2017

This work presents PRAC – Probabilistic Action Cores – an interpreter for naturallanguage instructions which is able to resolve vagueness and ambiguity in natural language and infer missing information pieces that are required to render an instruction executable by a robot.

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