Markov Logic in Natural Language Processing: Theory, Algorithms, and Applications
Hoifung Poon · North American Chapter of the Association for Computational Linguistics · 2010
Natural languages are characterized by rich relational structures and tight integration with world knowledge. As the field of NLP/CL moves towards more complex and challenging tasks, there has been increasing interest in applying joint inference to leverage such relations and prior knowledge. Recent work in statistical relational learning (a.k.a. structured prediction) has shown that joint inference can not only substantially improve predictive accuracy, but also enable effective learning with little or no labeled information. Markov logic is the unifying framework for statistical relational learning, and has spawned a series of successful NLP applications, ranging from information extraction to unsupervised semantic parsing. In this tutorial, I will introduce Markov logic to the NLP community and survey existing NLP applications. The target audience of the tutorial is all NLP researchers, students and practitioners. The audience will gain the ability to efficiently develop state-of-the-art solutions to NLP problems using Markov logic and the Alchemy open-source software.