• Corpus ID: 14585127

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… 
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cs.utexas.edu
18 Citations
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18 Citations

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References

SHOWING 1-10 OF 21 REFERENCES

Sound and Efficient Inference with Probabilistic and Deterministic Dependencies

MC-SAT is an inference algorithm that combines ideas from MCMC and satisfiability, based on Markov logic, which defines Markov networks using weighted clauses in first-order logic and greatly outperforms Gibbs sampling and simulated tempering over a broad range of problem sizes and degrees of determinism.

Composition in Distributional Models of Semantics

This article proposes a framework for representing the meaning of word combinations in vector space in terms of additive and multiplicative functions, and introduces a wide range of composition models that are evaluated empirically on a phrase similarity task.

Lifted First-Order Belief Propagation

This paper proposes the first lifted version of a scalable probabilistic inference algorithm, belief propagation, based on first constructing a lifted network, where each node represents a set of ground atoms that all pass the same messages during belief propagation.

Probabilistic theorem proving

This work proposes the first method that has the full power of both graphical model inference and first-order theorem proving (in finite domains with Herbrand interpretations), and proposes an algorithm for approximate PTP, which shows that it is superior to lifted belief propagation.

Towards a Formal Distributional Semantics: Simulating Logical Calculi with Tensors

This paper discusses how the canonical isomorphism between tensors and multilinear maps can be exploited to simulate a full-blown quantifier-free predicate calculus using tensors.

Wide-Coverage Semantic Analysis with Boxer

Boxer is an open-domain software component for semantic analysis of text, based on Combinatory Categorial Grammar and Discourse Representation Theory, which shows that bridging references and pronouns are not resolved in most cases.

A SICK cure for the evaluation of compositional distributional semantic models

This work aims to help the research community working on compositional distributional semantic models (CDSMs) by providing SICK (Sentences Involving Compositional Knowldedge), a large size English benchmark tailored for them.

Towards Efficient Sampling: Exploiting Random Walk Strategies

It is shown that random walk SAT procedures often do reach the full set of solutions of complex logical theories and how the sampling becomes near-uniform is shown.

Markov Logic: An Interface Layer for Artificial Intelligence

Most subfields of computer science have an interface layer via which applications communicate with the infrastructure, and this is key to their success, but this interface layer has been missing in AI.

From Frequency to Meaning: Vector Space Models of Semantics

The goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs, and to provide pointers into the literature for those who are less familiar with the field.