Fine-Grained Opinion Extraction with Markov Logic Networks

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.

Fine-grained Opinion Extraction with Mixed Network Model

“…We generate the dependency path and distance (the words' number between two different spans) from the document using Stanford coreNLP [14]. Because we chose a different corpus from [13], our classifier is trained by LIBSVM [15] instead of LIBLINEAR in [13] to fit the corpus's size. To further extract entities with our mixed network model, the CRFs result is used as input.…”

“…Relation Extraction Method in [13]: The main practice of this method is to adopt both lexical features and dependency path to train the relation classifier. We generate the dependency path and distance (the words' number between two different spans) from the document using Stanford coreNLP [14].…”

“…Their idea are similar to Y&C, but they used MLN model instead of the ILP one. The RMEM we proposed has the same input as [5,13], which is the preliminary extraction result of the CRFs, but we our model connects two kinds of relations which can provide more information.…”

“…For relation extraction, we implement the Adj method in [5] and the relation extraction method in [13]. These methods are used in previous researches which have similarity to our work, thus they are suitable for comparison.…”

“…In [5], Y&C proposed the first joint inference model in which a sequence labeling method was used to extract entities roughly, then relation features are added, and an ILP formulation was used to jointly evaluate whether an entity should be extracted. The [13] proposed a MLN model to tackle the extraction problem. Their idea are similar to Y&C, but they used MLN model instead of the ILP one.…”