Fine-Grained Opinion Extraction with Markov Logic Networks

Luis Gerardo Mojica and Vincent Ng.
Proceedings of the 14th IEEE International Conference on Machine Learning and Applications, pp. 271-276, 2015.

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Abstract

Markov Logic Networks, a joint inference framework that combines logical and probabilistic representations, enable effective modeling of the dependencies that exist between different instances of a data sample. While its ability to capture relational dependencies makes it an ideal framework for predicting the structures inherent in many natural language processing (NLP) tasks, it is arguably underused in NLP, especially in comparison to other joint inference frameworks such as integer linear programming. In this paper, we present the first Markov logic model for the NLP task of fine-grained opinion extraction that exploits a factuality lexicon. When evaluated on a standard evaluation corpus, our approach surpasses a state-of-the-art approach in performance.

BibTeX entry

@InProceedings{Mojica+Ng:15a,
  author = {Mojica, Luis Gerardo and Vincent Ng},
  title = {Fine-Grained Opinion Extraction with Markov Logic Networks},
  booktitle = {Proceedings of the 14th IEEE International Conference on Machine Learning and Applications},
  pages = {271--276}, 
  year = 2015}