The task emotion-cause pair extraction deals with finding all emotions and the corresponding causes from emotion texts. Existing joint methods solve it as multi-task learning, which introduces two auxiliary tasks (i.e., emotion extraction and cause extraction) to make use of task correlations for their mutual benefits. However, these methods focus on capturing such correlations by sharing parameters in an implicit way, not only have a limitation of cannot explicitly model their information interaction, but also suffer from low interpretability. Towards these issues, we propose a multi-task sequence tagging framework, which can extract emotions with the associated causes simultaneously by encoding their distances into a novel tagging scheme. In addition, the output of both auxiliary tasks can be directly used as inductive bias, to refine the tag distribution for benefiting emotion-cause pair extraction, so that the information exchange between them can be more explicit and interpretable. Results show that our model achieves the best performance, outperforming a number of competitive baselines by at least 1.03% ($p< 0.01$) in$F_1$score. The comprehensive analysis further confirms the superiority and robustness of our model.

Emotion-cause pair extraction aims to extract all potential pairs of emotions and corresponding causes from unannotated emotion text.Most existing methods are pipelined framework, which identifies emotions and extracts causes separately, leading to a drawback of error propagation.Towards this issue, we propose a transition-based model to transform the task into a procedure of parsing-like directed graph construction.The proposed model incrementally generates the directed graph with labeled edges based on a sequence of actions, from which we can recognize emotions with the corresponding causes simultaneously, thereby optimizing separate subtasks jointly and maximizing mutual benefits of tasks interdependently.Experimental results show that our approach achieves the best performance, outperforming the state-of-the-art methods by 6.71% (p < 0.01) in F 1 measure.

The task of emotion-cause pair extraction deals with finding all emotions and the corresponding causes in unannotated emotion texts.Most recent studies are based on the likelihood of Cartesian product among all clause candidates, resulting in a high computational cost.Targeting this issue, we regard the task as a sequence labeling problem and propose a novel tagging scheme with coding the distance between linked components into the tags, so that emotions and the corresponding causes can be extracted simultaneously.Accordingly, an end-to-end model is presented to process the input texts from left to right, always with linear time complexity, leading to a speed up.Experimental results show that our proposed model achieves the best performance, outperforming the state-of-the-art method by 2.26% (p < 0.001) in F 1 measure.

This paper presents the design and construction of a large-scale target-based sentiment annotation corpus on Chinese financial news text. Different from the most existing paragraph/document-based annotation corpus, in this study, target-based fine-grained sentiment annotation is performed. The companies, brands and other financial entities are regarded as the targets. The clause reflecting the profitability, loss or other business status of financial entities is regarded as the sentiment expression for determining the polarity. Based on high quality annotation guideline and effective quality control strategy, a corpus with 8,314 target-level sentiment annotation is constructed on 6,336 paragraphs from Chinese financial news text. Based on this corpus, several state-of-the-art sentiment analysis models are evaluated.