USSA : A Unified Table Filling Scheme for Structured Sentiment Analysis (2023)

Abstract: Most previous studies on Structured Sentiment Analysis (SSA) have cast it as a problem of bi-lexical dependency parsing, which cannot address issues of overlap and discontinuity simultaneously. In this paper, we propose a niche-targeting and effective solution. Our approach involves creating a novel bi-lexical dependency parsing graph, which is then converted to a unified 2D table-filling scheme, namely USSA. The proposed scheme resolves the kernel bottleneck of previous SSA methods by utilizing 13 different types of relations. In addition, to closely collaborate with the USSA scheme, we have developed a model that includes a proposed bi-axial attention module to effectively capture the correlations among relations in the rows and columns of the table. Extensive experimental results on benchmark datasets demonstrate the effectiveness and robustness of our proposed framework, outperforming state-of-the-art methods consistently.

## introduction structured sentiment analysis (ssa) aims to identify all opinion tuples within a given sentence. an opinion tuple (h, t, e, p) denotes a group of four elements: the holder h expresses a sentiment polarity p towards an opinion target t through a sentiment expression e. as shown in figure 1 (a), an example involving two opinion tuples illustrates the definition of ssa. ssa is more challenging than other related tasks because it requires identifying all four elements of the tuple and may involve overlapping or discontinuous elements. for example, aspectbased sentiment analysis @xcite @xcite mostly identifies flat aspect and opinion terms, and opinion mining @xcite identifies opinion tuples without the sentiment polarity. an example of structured sentiment analysis, which contains both overlapping and discontinuous situations about two expressions. (b) ambiguous expressions when using head-first bi-lexical dependency parsing method @xcite . (c) our proposed unified dependency parsing formulation. e-neg implies the edge where ending connects to starting (e.g., @xmath0. h-s/e denotes the starting/ending boundary of holder and expression, and t-s/e denotes the same for target and expression. * -nw indicates the next word. most of the existing methods cast the ssa task as the bi-lexical dependency parsing problem. unfortunately, the conversion is lossy, as it cannot address issues of overlap and discontinuity concurrently. for the example in figure 1(a) , there exist two overlapping foot_0 e foot_1 , i.e., {really, rude} and {really, long, winded}, and the latter is discontinuous. @xcite proposed a dependency parsing method namely head-first as illustrated in figure 1 @xcite . however, inherent ambiguity occurs in the dependency graph, as the method incorrectly predicts two overlapping e as one single e (i.e., {really, rude, long, winded}). in other words, the method may not be able to distinguish between two overlapping entities 4 in ssa. another dependency parsing method proposed by @xcite aims to identify the starting and ending positions of boundaries, but cannot identify discontinuous entities. statistics on benchmark datasets show the amount of opinion tuples involving overlapping or discontinuous problem in to resolve the kernel challenges (i.e., overlap and discontinuity) existing in ssa, we carefully construct a novel bi-lexical dependency parsing graph as shown in figure 1(c ). the graph comprises two types of edge: relation prediction (rp) and token extraction (te). rp mainly handles entity boundary identification and relation prediction, and it solves the overlap problem. specifically, e-pos/neg/neu edge connects ending and starting words (e.g., @xmath1. h-s/e edge marks the starting/ending boundary of h and e, and t-s/e edge is the same for t and e. another edge type te identifies all tokens within a given entity boundary, resolving the discontinuity problem. specifically, * -nw edges indicate the next word, meaning that the two words are consecutively joined as a segment of one entity. furthermore, we convert our proposed dependency parsing graph to a unified 2d table filling scheme, namely ussa as illustrated in figure 2 . specifically, we use the start position of each edge as the x-coordinate, the end position as the ycoordinate, and the type of edge as the relationship label in the table. thus, the table is divided into lower and upper triangular regions, corresponding to rp and te, respectively. based on the ussa scheme, we further develop 4 in ssa, an entity stands for a holder/target/expression. table filling. the table is divided into lower and upper triangular regions for relation prediction and token extraction, respectively. a model for ssa. first, multilingual bert and bi-directional lstm (bilstm) are used to provide contextualized word representations, based on which we construct a 2-dimensional (2d) table for word pairs. then, we observe that the relations have a strong correlation in the abscissa and ordinate of the table as shown in figure 2 . we propose a bi-axial attention module to effectively capture these correlations. finally, a predictor is employed to determine the relations between word pairs. we conduct extensive experiments on five benchmarks, including norec fine @xcite , multib eu , multib ca @xcite , mpqa @xcite and ds unis @xcite . our model demonstrates superior performance on all datasets, establishing a new sota method for ssa task. our contributions are highlighted as follows: ## related work structured sentiment analysis (ssa) can be divided into several sub-tasks, including extracting entities, determining the relationship between the entities, and assigning polarity. some previous research in opinion mining (om) has focused on extracting holders, targets, and expressions and identifying their relations, mainly utilizing the mpqa dataset @xcite . previous studies have explored different methods to tackle this task, like a bilstm-crf model @xcite that predicts the word-wise opinion role label and identifies the relations, an end-to-end bert-based model @xcite , a transition-based approach @xcite using pre-defined actions, and a unified span-based model @xcite that addresses overlap issues. all of these approaches, however, ignore the sentiment polarity classification subtask. in aspect based sentiment analysis (absa), several studies have attempted to unify multiple subtasks. some examples include aspect and opinion term co-extraction (aote) @xcite @xcite @xcite which combines target and expression extraction tasks, aspect-sentiment pair extraction (aspe) @xcite @xcite which combines target extraction and sentiment classification, and most recent aspect sentiment triplet extraction (aste) @xcite which further integrates multiple subtasks. these methods can generally be categorized into three groups: pipeline @xcite , end-to-end @xcite @xcite @xcite @xcite and mrc-based @xcite @xcite . however, these methods primarily focus on flat entities and ignore holder extraction. to this aim, @xcite originally cast the ssa task as a bi-lexical dependency parsing problem. nonetheless, as aforementioned in section 1, the conversion is lossy because it cannot distinguish between two overlapping entities. to address this issue, @xcite proposed another parsing method but unfortunately it cannot identify the discontinuous entities. @xcite identified the issue of nest and proposed to decode the sentiment graph from the text directly. therefore, we propose a novel dependency parsing type # relation meaning of word pair (w i , w j ) relation prediction 1 2 3 e-pos e-neg e-neu boundary words of expression with positive/negative/neutral polarity 4 5 6 h-s h-e h-se starting or ending boundary of holder and corresponding expression 7 8 9 t-s t-e t-se starting or ending boundary of target and corresponding expression token extraction 10 11 12 e-nw h-nw t-nw specific tokens of expression/holder/target by indicating w j is the next word of w i 13 ⊥ no above relations ## unified table filling scheme in this section, we introduce the problem formulation of the ssa task, explain the table filling scheme ussa, and show how to decode opinion tuples from the ussa tagging results. ## model structure this section elaborates upon our model, as depicted in figure 4 , which is designed to effectively integrate the ussa scheme. our model is mainly composed of four components: the encoder layer, the word-pair representation layer, the refining strategy, and the prediction layer. ## discussion in this section, we will conduct a deeper analysis to answer the following questions. ## conclusion in this paper, we propose a novel bi-lexical dependency parsing graph and convert it to a unified 2d table-filling scheme, namely ussa to address the overlapping and discontinuous issues simultaneously. we also develop a model that includes a novel bi-axial attention module to better refine the word-pair representation. additionally, our proposed framework may serve as an inspiration for other tasks involving the extraction of tuples that both present overlap and discontinuity challenges.