HITSZ - HLT at S em E val-2022 Task 10: A Span-Relation Extraction Framework for Structured Sentiment Analysis (2022)

Abstract: This paper describes our system that participated in the SemEval-2022 Task 10: Structured Sentiment Analysis, which aims to extract opinion tuples from texts.A full opinion tuple generally contains an opinion holder, an opinion target, the sentiment expression, and the corresponding polarity. The complex structure of the opinion tuple makes the task challenging. To address this task, we formalize it as a span-relation extraction problem and propose a two-stage extraction framework accordingly. In the first stage, we employ the span module to enumerate spans and then recognize the type of every span. In the second stage, we employ the relation module to determine the relation between spans. Our system achieves competitive results and ranks among the top-10 systems in almost subtasks.

## introduction sentiment analysis, also called opinion mining, aims to analysis people's attitudes and emotions towards specific targets, such as products, organizations, events, etc @xcite . it has become an important research field in natural language processing @xcite @xcite . structured sentiment analysis. @xcite formally defines a complete opinion as a quadruple (h, t, e, p) where h is a holder who expresses a polarity p towards a target t through a sentiment expression e. figure 1 presents examples of opinion quadruples. on the basis of this definition, @xcite formally establishes a benchmark for structured sentiment analysis. this benchmark consists of two tracks, the monolingual track and the crosslingual track, and we participate the monolingual track. in this paper, we cast this task as a span-relation extraction problem @xcite , which is a formalization that has been widely used in many figure 1 : examples of opinion quadruples @xcite . in addition, we employ span pruning @xcite to reduce the computation of the second stage. finally, opinion quadruples are decoding from the results of two stages. our system achieves competitive results and ranks among the top-10 systems in almost subtasks. ## related work span extraction is a fundamental method for many tasks, such as named entity recognition, aspectlevel sentiment analysis, etc. this method performs element extraction by enumerating all possible spans and then determining the type of spans. @xcite attempts to determine the type of spans by encoding all possible spans into a representation of the same size. sohrab and miwa (2018) also enumerate all potential spans and then use a deep network to classify them. @xcite leverage the coreference and relation type confidences to enhance the representation of spans. @xcite added the task of span boundary detection to improve the sensitivity of the model to span boundaries. this approach was able to produce higher quality candidate spans. span-relation extraction for sentiment tasks focuses on extracting categories of spans and relationships between spans, such as extracting relationships between entities and extracting aspect sentiment triplet. @xcite try to solve the aspect sentiment triplet extraction problem using a two-stage pipeline. the first stage extracts the target as well as its polarity and opinion, using the bioes annotation method.the second stage then couples the extracted target and opinion terms to determine their paired sentiment relation. however, this method may suffer from the problem of error propagation. end-to-end methods @xcite ) can extract both span and their relationships. however, previous work has usually used word-to-word interactions to predict sentiment relationships. the disadvantage of this approach is that it ignores the sentiment consistency of the entire span. the method proposed by @xcite can accurately enumerate all the span representations with high likelihood and then predict the sentiment relationship between them. this approach can mitigate the impact of errors in the span extraction step on subsequent relationship prediction, while it also preserves the sentiment consistency of the entire span when predicting relationships ## our system given the input text, we first obtain its contextualized representation through a pre-trained language model, bert @xcite or roberta @xcite . then we input the contextualized representation into the span module and the relation module in turn to extract spans and detect relations. ## experiments the monolingual track @xcite provides 7 structured sentiment datasets in five languages. their statistics are listed on it is worth noting that there are discontinuous spans in the norec fine and ds unis datasets. for example, in "it looks again like umuc will do anything for money", "looks again" and "do anything" are annotated as the same sentiment expression. ## conclusions this paper describes our system for structured sentiment analysis. we formalize the task as a spanrelation extraction problem and propose a twostage extraction approach, which consists of a span module and a relation module. experimental results demonstrate the effectiveness of our approach.