Markov Logic Networks for Text Mining: A Qualitative and Empirical Comparison with Integer Linear Programming

Luis Gerardo Mojica and Vincent Ng.
Proceedings of the 10th International Conference on Language Resources and Evaluation, pp. 4388-4395, 2016.

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Abstract

Joint inference approaches such as Integer Linear Programming (ILP) and Markov Logic Networks (MLNs) have recently been successfully applied to many natural language processing (NLP) tasks, often outperforming their pipeline counterparts. However, MLNs are arguably much less popular among NLP researchers than ILP. While NLP researchers, as users of machine learning, do not have to understand the theoretical underpinnings of these joint inference frameworks, it is imperative that we understand which of them should be applied under what circumstances. With the goal of helping NLP researchers better understand the relative strengths and weaknesses of MLNs and ILP; we will compare them along different dimensions of interest, such as expressiveness, ease of use, scalability, and performance. To our knowledge, this is the first systematic comparison of ILP and MLNs on an NLP task.

BibTeX entry

@InProceedings{Mojica+Ng:16a,
  author = {Mojica, Luis Gerardo and Vincent Ng},
  title = {Markov Logic Networks for Text Mining: A Qualitative and Empirical Comparison with Integer Linear Programming},
  booktitle = {Proceedings of the 10th International Conference on Language Resources and Evaluation},
  pages = {4388--4395}, 
  year = 2016}