A Shared-Private Representation Model with Coarse-to-Fine Extraction for Target Sentiment Analysis (2020)

Abstract: Target sentiment analysis aims to detect opinion targets along with recognizing their sentiment polarities from a sentence. Some models with span-based labeling have achieved promising results in this task. However, the relation between the target extraction task and the target classification task has not been well exploited. Besides, the span-based target extraction algorithm has a poor performance on target phrases due to the maximum target length setting or length penalty factor. To address these problems, we propose a novel framework of Shared-Private Representation Model (SPRM) with a coarse-to-fine extraction algorithm. For jointly learning target extraction and classification, we design a Shared-Private Network, which encodes not only shared information for both tasks but also private information for each task. To avoid missing correct target phrases, we also propose a heuristic coarse-to-fine extraction algorithm that first gets the approximate interval of the targets by matching the nearest predicted start and end indexes and then extracts the targets by adopting an extending strategy. Experimental results show that our model achieves state-of-the-art performance.

## introduction target sentiment analysis aims to detect the opinion targets explicitly mentioned in the sentences, referred to as target extraction, and predict the sentiment polarities over the opinion targets, referred to as target classification. for example, in the sentence "i love windows 7 which is a vast improvement over vista.", the user mentions two opinion targets, namely, "windows 7" and "vista", and expresses positive sentiment over the first target, and negative sentiment over the second one. ## model to solve the aforementioned issues, we simultaneously learn shared and private features for the target extraction and classification in a unified framework, in which a coarse-to-fine extraction algorithm is designed. in this paper, we propose a novel model of shared-private representation model (sprm) shown in fig. 2 , which encodes the shared and private information of the target extraction sub-task and the target classification sub-task effectively at a lower cost. specifically, a shared bert network is designed to encode as much shared information of both sub-tasks as possible, and two private bilstms are introduced to get the supplementary private representations for each task with fewer parameters than bert. moreover, we design a coarse-to-fine algorithm that first gets the approximate interval of the targets by matching the nearest predicted start and end indexes without any thresholds and then gets the final targets by extending the interval if the adjacent words are predicted as start/end boundaries. with the algorithm, targets can be extracted with reasonable length, since the nearest strategy avoids overlong targets while the extending strategy avoids missing target phrases. ## conclusion in this paper, we propose a shared-private representation model (sprm) with coarse-to-fine extraction for target sentiment analysis. to encode the information of the two sub-tasks of target sentiment analysis, a shared-private network has been proposed to learn shared features as well as private features. moreover, we designed a coarse-to-fine extraction algorithm, which extracts targets without thresholds and adopts an extending strategy for better extracting target phrases. experiments on three benchmark datasets show the effectiveness of sprm.