A Joint Coreference-Aware Approach to Document-Level Target Sentiment Analysis

Most existing work on aspect-based sentiment analysis (ABSA) focuses on the sentence level, while research at the document level has not received enough attention. Compared to sentence-level ABSA, the document-level ABSA is not only more practical but also requires holistic document-level understanding capabilities such as coreference resolution. To investigate the impact of coreference information on document-level ABSA, we conduct a three-stage research for the document-level target sentiment analysis (DTSA) task: 1) exploring the effectiveness of coreference information for the DTSA task; 2) reducing the reliance on manually annotated coreference information; 3) alleviating the evaluation bias caused by missing the coreference information of opinion targets. Specifically, we first manually annotate the coreferential opinion targets and propose a multi-task learning framework to model the DTSA task and the coreference resolution task jointly. Then we annotate the coreference information with ChatGPT for joint training. Finally, to address the issue of missing coreference targets, we modify the metric from strict matching to a loose matching method based on the clusters of targets. The experimental results demonstrate our framework's effectiveness and reflect the feasibility of using ChatGPT-annotated coreferential entities and the applicability of the modified metric. Our source code is publicly released at https://github.com/NUSTM/DTSA-Coref .