FREERL: Fusion relation embedded representation learning framework for aspect extraction

Abstract

Opinion object-attribute extraction is one of the fundamental tasks of fine-grained sentiment analysis. It is accomplished by identifying opinion aspect entities (including object entities and attribute entities) and then aligning object entities to attribute entities. Recent studies on knowledge graphs have shown that by adding the embeddings of semantic structures between opinion aspect entities, structure-based learning models can achieve better performance in link-prediction than traditional methods. The studies, however, focused only on learning semantic structures between aspect entities, did not take language expression features into account. In this paper, we propose the Fusion RElation Embedded Representation Learning (FREERL) framework, by which, one can fuse semantic structures and language expression features such as statistical co-occurrence or dependency syntax, into the embeddings of object entities and attribute entities. The obtained embeddings are then used to align object-attribute pairs and to predict new pairs in a zero-shot scenario. Experimental results on the datasets of COAE2014 and COAE2015 show that the best results in our framework achieve 12.1% and 32.1% improvements over the baselines, respectively.