Wenxuan Shi, Fei Li, Jingye Li, Hao Fei, Donghong Ji Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, China {shiwenxuan,lifei_csnlp,theodorelee,hao.fei,dhji}@whu.edu.cn Abstract The state-of-the-art model for structured sentiment analysis casts the task as a dependency parsing problem, which has some limitations: (1) The label proportions for span prediction and span relation prediction are imbalanced. (2) The span lengths of sentiment tuple components may be very large in this task, which will further exacerbates the imbalance problem. (3) Two nodes in a dependency graph cannot have multiple arcs, therefore some overlapped sentiment tuples cannot be recognized. In this work, we propose nichetargeting solutions for these issues. First, we introduce a novel labeling strategy, which contains two sets of token pair labels, namely essential label set and whole label set. The essential label set consists of the basic labels for this task, which are relatively balanced and applied in the prediction layer. The whole label set includes rich labels to help our model capture various token relations, which are applied in the hidden layer to softly influence our model. Moreover, we also propose an effective model to well collaborate with our labeling strategy, which is equipped with the graph attention networks to iteratively refine token representations, and the adaptive multi-label classifier to dynamically predict multiple relations between token pairs. We perform extensive experiments on 5 benchmark datasets in four languages. Experimental results show that our model outperforms previous SOTA models by a large margin. 1 1 Introduction Structured Sentiment Analysis (SSA), which aims to predict a structured sentiment graph as shown in Figure 1(a), can be formulated into the problem of tuple extraction, where a tuple ( h, e, t, p ) denotes a holder h who expressed an expression e towards a target t with a polarity p . SSA is a more challenging task, because other related tasks only focus  Corresponding author 1 Our code is available at https://github.com/ Xgswlg/TGLS Moscow Government has expressed the wish to import the Mongolian meat Neutral Holder Expression Target Moscow Government has expressed the wish to import the Mongolian meat Exp : Neutral Holder Holder Exp : Neutral Exp : Neutral Target Target Target Target on extracting part of tuple components or the text spans of the components are short. For example, Opinion Role Labeling (Katiyar and Cardie, 2016; Xia et al., 2021) does not include the extraction of sentiment polarities, and Aspect-Based Sentiment Analysis (ABSA) (Pontiki et al., 2014; Wang et al., 2016) extracts the aspect and opinion terms typically consisting of one or two words. The state-of-the-art SSA model is proposed by Barnes et al. (2021), which casts the SSA task as the dependency parsing problem and predicts all tuple components as a dependency graph (Figure 1(b)). However, their method exists some shortages. Taking Figure 1(b) as example, only 2 arcs (e.g., expressed  import and expressed  Moscow) are related to span linking relation prediction (i.e., the relations between expressions and holders or targets), while much more other arcs are related to span prediction (e.g., import  the and import  meat). Such imbalanced labeling strat-4232 Moscow Government has expressed the wish to import the Mongolian meat Span Label Span Label Span Label Rel Label Rel Label Rel Label Rel Label Span Label Span Label [CLS] Moscow Government has expressed the wish to import the Mongolian meat [CLS]-related Label Figure 2: The whole label set contains the labels for span prediction and span relation prediction, as well as the [CLS] -related labels that connect a sentinel [CLS] token with the holder, target and expression tokens. egy will make the model pay more attention on span prediction but less on span relation prediction. Furthermore, since the span lengths of sentiment tuple components may be very large in the SSA task, the label imbalanced problem will become more severe. Besides, the dependency parsing graph is not able to deal with multi-label classification, since it does not allow multiple arcs to share the same head and dependent tokens. Therefore, some overlapped sentiment tuples cannot be recognized. The statistics of span length and multi-label problems are listed in Table 1. To alleviate the label imbalance problem in Barnes et al. (2021), we propose a novel labeling strategy that consists of two parts: First, we design a set of labels called essential label set (Figure 1(c)), which can be considered as the basic label set for decoding SSA tuples, since it only includes the labels to tag the boundary tokens of spans. As seen, the proportion of span prediction labels and span relation prediction labels are relatively balanced, so that we can mitigate the label imbalance problem and meanwhile keep the basic ability of extracting sentiment tuples if the essential label set is learnt in the final prediction layer of our model. However, the labels related to recognize non-boundary tokens of SSA components are also important. For instance, they can encode the relations between the tokens inside the spans, which may benefit the extraction of the holders, expressions or targets with long text spans. To this end, we design another label set called whole label set (Figure 2), which includes richer labels to fully utilize various information such as the relations among boundary tokens, non-boundary tokens, the tokens within a span, the tokens across different spans. Moreover, since the dependency-based method (Barnes et al., 2021) only considers the local relation between each pair of tokens, we add the labels between [CLS] and other tokens related to sentiment tuples into our whole label set, in order to utilize sentence-level global information. Considering that if the whole label set is directly applied on the output label for training, the label imbalance problem may occur again. We instead employ the whole label set in a soft and implicit fashion by applying it on the hidden layer of our model. To well collaborate with our labeling strategy, we also propose an effective token graph model, namely TGLS ( T oken G raph with a novel L abeling S trategy), which uses rich features such as word, part-of-speech tags and characters as inputs and yields contextualized word representations by BiLSTM and multilingual BERT(Devlin et al., 2018). In the hidden layer, we build a multi-view token graph, which has four views corresponding to different relations in the whole label set and each view is a graph attention network (Velickovic et al., 2017) with token representations as the nodes. In the prediction layer, we introduce a novel adaptive multi-label classifier to extract all the sentiment tuples no matter that they are overlapped or not. We conduct extensive experiments on five benchmarks, including NoReC Fine (vrelid et al., 2020), MultiB EU , MultiB CA (Barnes et al., 2018), MPQA (Wiebe et al., 2005) and DS Unis (Toprak et al., 2010). The resluts show that our TGLS model outperforms the SOTA model by a large margin. In summary, our main contributions include:  We design a novel labeling strategy to address the label imbalance issue in prior work. Concretely, we employ the whole label set and essential label set in the hidden and prediction layer respectively, achieving a balance 4233 between the label variety and label imbalance. We propose an effective token graph model to well collaborate with our labeling strategy, which learns the token-token relations via multi-view token graph networks and reasons the labels between each pair of words using the adaptive multi-label classifier for both overlapped and non-overlapped tuple extraction. The experimental results show that our model has achieved the SOTA performance in 5 datasets for structured sentiment analysis, especially in terms of the end-to-end sentiment tuple extraction. The task of the Structured Sentiment Analysis (SSA) can be divided into sub-tasks such as span extraction of the holder, target and expression, relation prediction between these elements and assigning polarity. Some existing works in Opinion Mining used pipeline methods to first extract spans and then the relations mostly on the MPQA dataset (Wiebe et al., 2005). For example, Katiyar and Cardie (2016) propose a BiLSTM-CRF model which is the first such attempt using a deep learning approach, Zhang et al. (2019) propose a transition-based model which identifies opinion elements by the human-designed transition actions, and Xia et al. (2021) propose a unified span-based model to jointly extract the span and relations. However, all of these works ignore the polarity classification sub-task. In End2End Aspect-Based Sentiment Analysis (ABSA), there are also some attempts to unify several sub-tasks. For instance, Wang et al. (2016) augment the ABSA datasets with sentiment expressions, He et al. (2019) make use of this data and models the joint relations between several sub-tasks to learn common features, and (Chen and Qian, 2020) also exploit interactive information from each pair of sub-tasks (target extraction, expression extraction, sentiment classification). However, Wang et al. (2016) only annotate sentiment-bearing words not phrases and do not specify the relationship between target and expression, it therefore may not be adequate for full structured sentiment analysis. Thus, Barnes et al. (2021) propose a unified approach in which they formulate the structured sentiment analysis task into a dependency graph parsing task and jointly predicts all components of a sentiment graph. However, as aforementioned, this direct transformation may be problematic as it may introduce label imbalance in span and relation prediction. Thus, we propose an effective graph model with a novel labeling strategy in which we employ a whole label set in the hidden layer to softly affect our model, and an essential label set in the prediction layer to address the imbalance issue. The design of our essential label set is inspired by the Handshaking Tagging Scheme (Wang et al., 2020), which is a token pair tagging scheme for entity and relation extraction. The handshaking tagging scheme involves only the labels related to the boundary tokens and enables a one-stage joint extraction of spans and relations. In our work, we modify the handshaking tagging scheme to use it for SSA. Furthermore, since the component span of this task is relatively long, only utilizing the boundary tokens cannot make full use of the annotation information, so we propose a new label set called whole label set, which together with essential label set constitutes our labeling strategy. Our essential label set only involves the labels related to the boundary tokens, therefore the label proportions for span prediction and span relation prediction are relatively balanced. Given a sentence " Moscow government has expressed the wish to import the Mongolian meat. ", the essential label set consists of the following labels:  Holder : Moscow  government  Exp:Neutral : expressed  Moscow  Target : import  meat  Exp Head to Holder Head : expressed  Moscow  Exp Tail to Holder Tail : wish  government  Exp Head to Target Head : expressed  import  Exp Tail to Target Tail : wish  meat where the Holder , Exp. and Target represent the three components of a sentiment tuple, the Head or Tail means the start or end token of a component, and the Neutral denotes the polarity. Our whole label set involves both the labels related to boundary and non-boundary tokens, as well as the labels related to [CLS] and all tokens in the sentiment tuples. Thus, our whole label set can be divided into three groups, span labels, relation labels and [CLS] -related labels. Given the sentence in Figure 2, the whole label set include the following labels:  Span Label : e.g. import  Mongolian  Rel Label : e.g. Moscow  expressed  [CLS] -related Label : e.g. [CLS]  expressed where the span and relation labels make our model be aware of the token relations inside and across the spans of sentiment components, and [CLS] related labels can help our model to capture the sentence-level global information. We apply whole labels in the hidden layer to softly embed the above information into our model, in order to avoid the potential label imbalance issue. We first decode all the expression-holder and expression-target pairs that meet the constraints of essential label set. In detail, we can get all component spans based on span prediction labels (e.g. Holder , Exp:Neutral and Target labels), then we decode all expression to holder or target pairs as long as it meets one of the corresponding relation prediction labels (e.g. for expression to holder pairs, the labels are Exp Head to Holder Head and Exp Tail to Hoder Tail ). After decoding all the component pairs, we enumerate all possible triples from pairs with the same expression, thus finally decode all the sentiment tuples. In this section, We formally present our proposed TGLS model in detail (Figure 3), which mainly consists of four parts, the encoder layer, the multiview token graph as the hidden layer, the adaptive multi-label classifier as the prediction layer and the hierarchical learning strategy to train our model. Consider the i th token in a sentence with n tokens, we represent it by concatenating its token embedding e wordi , part-of-speech (POS) embedding e posi , lemma embedding e lemmai , and character-level embedding e chari together: w i = e wordi  e posi  e lemmai  e chari (1) where  denotes the concatenation operation. The character-level embedding is generated by the convolution neural networks (CNN) (Kalchbrenner et al., 2014). Then, we employ bi-directional LSTM (BiLSTM) to encode the vectorial token representations into contextualized word representations: h i = BiLSTM ( w i ) (2) where h i is the token hidden representation. Moreover, in the same way as previous work (Barnes et al., 2021), we also enhance token representations with pretrained contextualized embeddings using multilingual BERT (Devlin et al., 2018). In this section, we propose a novel multi-view token graph as our hidden layer, which includes four views, span graph, relation graph, [CLS] -related graph and vanilla GAT graph, and each view is full connected with the attention scoring weights as graph edges and the token representations as graph nodes. Recall that the whole label set is applied in this layer, which includes three groups of labels (span, relation and [CLS] -related labels). Thus, three views of graphs (span, relation and [CLS] related graph) are used to digest information from three groups of labels respectively, while one view (vanilla GAT graph) is not assigned for any specific task, as the method in vanilla graph attention network (GAT) (Velickovic et al., 2017). Formally, we represent the latent token graph G as follows: G = (cid:0) V , SG o , SG s , SG r , SG c (cid:1) (3) where superscript G denotes the graph layer, V is the set of tokens, SG o is the attention scoring matrix in vanilla GAT, SG s , SG r and SG c are the attention scoring matrices used to capture information from span, relation and [CLS] -related labels respectively. Without loss of generality, we employ SG = { SG o , SG s , SG r , SG c } unifiedly to represent the four matrices. In this section, we introduce the process that we induce the edges of our multi-view token graphs (i.e. four attention scoring matrices SG ) using a mechanism of attention scoring. Attention Scoring Our attention matrices are produced by a mechanism of attention scoring which takes two token representations h i , h j as the input, and for the attention matrix corresponding to a certain view v  { o, s, r, c } , we first map the tokens to q v,i and k v,j with two multi-layer perceptions (MLP): q v,i , k v,j = MLP qv ( h i ) , MLP kv ( h j ) (4) Then we apply the technique of Rotary Position Embedding (RoPE) (Su et al., 2021) to encode the relative position information. Thus, for the graph of view v , the attention score SG v,ij between token i and j can be calculated as follows: SG v,ij = ( q v,i )  R j  i k v,j (5) where R j  i can incorporate explicit relative positional information into the attention score SG v,ij . And in the same way as calculating SG v,ij , we can produce the scores of all views and all token pairs, thus inducing the whole graph edges SG : SG = (cid:110) SG v,ij | v  { o, s, r, c } , 1  i, j  n (cid:111) (6) where n is the length of the sentence. The process that the whole label set learnt by attention scoring matrices SG s , SG r and SG c through a multilabel adaptive-threshold loss will be introduced in Section 4.4. Considering that the attention scoring matrix SG now fuses rich information, we naturally think of applying a multi-hop reasoning to obtain more informative token representations. Concretely, we first apply a softmax on our adjacency attention matrix SG , then the computation for the representation u l +1 i of the token i at the ( l + 1) th layer, which takes the representations from previous layer as input and outputs the updated representations, can be defined as: A v = Softmax (cid:0) SG v (cid:1) , v  { o, s, r, c } (7) u l +1 i =    1 N (cid:88) v (cid:88) j N vi A v,ij W vl u lj   (8) where W vl is the trainable weight, N vi is the neighbor of token i in graph of view v ,  is the ReLU activation function. Considering that the previous sota model (Barnes et al., 2021) is not able to deal with multi-label classification as aforementioned, we propose a novel adaptive multi-label classifier as our prediction layer to identify possible essential labels for each token pair. Firstly, we take a shortcut connection between the outputs of the encoder layer and graph layer to get the final representation c i = h i  u i for each token. And by taking c i as the input, we calculate the attention scoring matrices SP based on the mechanism of attention scoring (cf. Eq.(4), Eq.(5) and Eq.(6)): SP = { SP r | r  R e } (9) 4236 where superscript P denotes the prediciton layer, R e denotes the essential label set. Then, we introduce a technique of adaptive thresholding, which produces a token pair dependent threshold to enable the prediction of the labels for each token pair. Adaptive Thresholding For a certain token pair with representations of c i , c j , the token pair dependent threshold T HP ij and the whole T HP are calculated as follows: T HP ij = (cid:0) q THi (cid:1)  R j  i k THj T HP = (cid:8) T HP ij | 1  i, j  n (cid:9) (10) where q THi = W q h i + b q , k THj = W k h j + b k , the W q , W k , b q and b k are the trainable weight and bias matrix, R j  i are calculated in the same way as Eq.(5), which is used to incorporate explicit relative positional information. Formally, for a certain token pair c i , c j , the essential label set is predicted by the following equation:  ij = (cid:8) r | SP r,ij > T HP ij , r  R e (cid:9) (11) where R e denotes the essential label set,  ij is the set of predicted labels of token pair c i , c j . In this section, we will propose a novel loss function, namely multi-label adaptive-threshold loss, to enable a hierarchical training process for our model and our labeling strategy (i.e. whole label set learnt by SG s , SG r and SG c in the hidden layer, essential label set learnt by SP in the prediction layer), which is based on a variant 2 of Circle loss (Sun et al., 2020), the difference is that we replace the fixed global threshold with the adaptive token pair dependent threshold to enable a flexible and selective learning of more useful information from whole label set. Take the hidden layer as an example. Actually, we also implement the adaptive thresholding (cf. Eq.(10)) in the hidden layer, where we compute all the token pair dependent threshold T HG = (cid:110) T HG ij | 1  i, j  n (cid:111) by taking the token representation h i and h j as the input. Then, the multi-label adaptive-threshold loss in hidden 2 The variant of Circle loss was proposed by Su on the website https://kexue.fm/archives/7359. layer can be calculated as follows: L w = (cid:88) i (cid:88) j>i log   e THG ij + (cid:88) r   negij e SG r,ij   + (cid:88) i (cid:88) j>i log   e  THG ij + (cid:88) r   posij e  SG r,ij   (12) where  posij  R w and  negij  R w are positive and negative classes involving whole labels that exist or not exist between token i and j . When minimizing L w , the loss pushes the attention score SG r,ij above the threshold T HG ij if the token pair possesses the label, while pulls below when it does not. 3 In a similar way we can calculate the loss L e in the prediction layer by taking T HP , SP as the inputs of the loss function. Thus the whole loss of our model can be calculated as follows: L all = L e +  L w (13) where the  is a hyperparameter to adjust the ratio of the two losses. For comparison with previous sota work (Barnes et al., 2021), we perform experiments on five structured sentiment datasets in four languages, including multi-domain professional reviews NoReC Fine (vrelid et al., 2020) in Norwegian, hotel reviews MultiB EU and MultiB CA (Barnes et al., 2018) in Basque and Catalan respectively, news MPQA (Wiebe et al., 2005) in English and reviews of online universities and e-commerce DS Unis (Toprak et al., 2010) in English. For fair comparison, we use word2vec skip-gram embeddings openly available from the NLPL vector repository 4 (Kutuzov et al., 2017) and enhance token representations with multilingual BERT (De-vlin et al., 2018), which has 12 transformer blocks, 12 attention heads, and 768 hidden units. Our network weights are optimized with Adam and we also conduct Cosine Annealing Warm Restarts learning 3 As aforementioned in Section 4.2, three of the attention scoring matrices and three groups of the whole labels have a one-to-one relationship, so here we can index the three matrices with the whole labels. 4 http://vectors.nlpl.eu/repository. rate schedule (Loshchilov and Hutter, 2016). We fixed the word embeddings during training process. The char embedding size is set to 100. The dropout rate of embeddings and other network components are set to 0.4 and 0.3 respectively. We employ 4-layer BiLSTMs with the output size set to 400 and 2-layer for multi-hop reasoning with output size set to 768. The learning rate is 3e-5 and the batch size is 8. The hyperparameter  in Eq.13 is set to 0.25 (cf. Section 6.2). We use GeForce RTX 3090 to train our model for at most 100 epochs and choose the model with the highest SF1 score on the validation set to output results on the test set. RACL-BERT Chen and Qian (2020) propose a relation-aware collaborative learning framework for end2end sentiment analysis which models the interactive relations between each pair of sub-tasks (target extraction, expression extraction, sentiment classification). Barnes et al. (2021) reimplement the RACL as a baseline for SSA task in their work. Head-first and Head-final 5 Barnes et al. (2021) cast the structured sentiment analysis as a dependency parsing task and apply a reimplementation of the neural parser by Dozat and Manning (2018), where the main architecture of the model is based on a biaffine classifier. The Head-first and Head final are two models with different setups in the parsing graph. Following previous SOTA work (Barnes et al., 2021), we use the Span F1, Targeted F1 and two Sentiment Graph Metrics to measure the experimental results. In detail, Span F1 evaluates how well these models are able to identify the holders, targets, and expressions. Targeted F1 requires the exact extraction of the correct target, and the corresponding polarity. Sentiment Graph Metrics include two F1 score, Non-polar Sentiment Graph F1 (NSF1) and Sentiment Graph F1 (SF1), which aims to measure the overall performance of a model to capture the full sentiment graph (Figure 1(a)). For NSF1, each sentiment graph is a tuple of (holder, target, expres-5 https://github.com/jerbarnes/sentiment_graphs. sion), while SF1 adds the polarity (holder, target, expression, polarity). A true positive is defined as an exact match at graph-level, weighting the overlap in predicted and gold spans for each element, averaged across all three spans. Moreover, for ease of analysis, we add an Overall Span F1 score which evaluates how well these models are able to identify all three elements of a sentiment graph with token-level F1 score. In this section, we introduce the main experimental results compared with three state-of-the-art models RACL-BERT (Chen and Qian, 2020), Head-first and Head-final models (Barnes et al., 2021). Table 2 shows that in most cases our model performs better than other baselines in terms of the Span F1 metrics across all datasets. The average improvement (  1 . 4 ) in Overall Span F1 score proves the effectiveness of our model in span extraction. Besides, there exists some significant improvements such as extracting holder on DS Unis (  6.3) and extracting expression on NoReC Fine (  4.7), but the extracting expression on DS Unis (  2.9) are poor. As for the metric of Targeted F1, although the Head-first model performs well on MPQA , our TGLS model is obviously more robust as we achieves superior performance on other 4 datasets. There are also extremely significant improvements such as on NoReC Fine (  6.2) and on MultiB CA (  5.6), it proves the capacity of our model in exact prediction of target and the corresponding polar. are important for comprehensively examining span, relation and polar predictions, our TGLS model achieves superior performance throughout all datasets in both NSF1 and SF1 score, especially on NoReC Fine (  7.2 and  6.4). And the average improvement (  4.5) in SF1 score verifies the excellent ability of our model in the end-to-end sentiment tuple extraction. In this section, we conduct extensive ablation studies on NoReC Fine to better understand independent contributions of different components in terms of span overall F1, targeted F1 and SF1 scores. Firstly, we remove each view of our graphs separately. As shown in Table 3, we observe that the [CLS] -related graph is effective in all three metrics which proves the importance of utilizing sentence-level global information. As we assumed, the span graph makes more contribution to the performance of span extraction (Span Overall F1) while the relation graph contributes more to end-to-end sentiment tuple extraction (SF1). And we also observe that the vanilla GAT graph makes consid-4239 erable improvement in SF1 score. Then, we test the effectiveness of the Rotary Position Embedding (RoPE) (Su et al., 2021). The results in Table 3 demonstrate that RoPE can make our model more sensitive to the relative positional information since it significantly improves the performance of exact target extraction (Targeted F1). Last, we replace the adaptive threshold with fixed global threshold, and we observe that the performance drops drastically in all three metrics, it suggests that the adaptive thresholding mechanism is very crucial for our model since the flexibility can allow our model to selectively learn more useful information for SSA task from whole labels. In this section we perform a deeper analysis on the models in order to answer three research questions: Experimental results in Table 2 show that our model performs significantly better in the SF1 score, which to some extent proves that our model can ensure the efficiency of relation extraction. However, there lacks a metric to directly quantify the ability in relation extraction and it is still a worthy question to explore how much of the improvement comes from our new model and how much from our new labeling strategy? To answer the question, we replace our labels with the dependency-parsing-based labels in head-final setting (Barnes et al., 2021) and experiment on all datasets in terms of a new relation prediction metric, where a true positive is defined as any span pair that overlaps the gold span pair and has the same relation. Table 4 shows that our new model achieves superior performance of relation prediction than the previous sota model (Barnes et al., 2021). Besides, with new labeling strategy, we can see that our model significantly improve the performance on all datasets compared with the model with replaced dependency-parsing-based labels. In this section, we experiment on five datasets to heuristically search for the appropriate value of hyperparameter  (cf. Eq.(13)). Figure 4 shows that all datasets achieve higher SF1 score with  between 0.1 and 0.5. We ended up fixing alpha to 0.25, since most datasets yield optimal results around this value. In addition, it is worth noting that when  is set to 0, which means that the whole labels are completely removed, the performance drops a lot, which once again proves the effectiveness of learning whole labels in the hidden layer. In this section, we experiment on NoReC Fine to further explore whether whole labels contribute to long span identification. Figure 5(a) evaluates the Expression F1 scores regarding to different expression lengths, we can find that whole labels helps most on those expressions with longer length. In Figure 5(b), we also report the SF1 scores regarding to different distances, that is, from the leftmost token in a tuple to the rightmost token, which shows a similar conclusion. In this paper, we propose a token graph model with a novel labeling strategy, consisting of the whole and essential label sets, to extract sentiment tuples for structured sentiment analysis. Our model is capable of modeling both global and local token pair interactions by jointly predicting whole labels in the hidden layer and essential labels in the output layer. More importantly, our modeling strategy is able to alleviate the label imbalance problem when using token-graph-based approaches for SSA. Experimental results show that our model overwhelmingly outperforms SOTA baselines and improves the performance of identifying the sentiment components with long spans. We believe that our labeling strategy and model can be well extended to other structured prediction tasks. We thank all the reviewers for their insightful comments. This work is supported by the National Natural Science Foundation of China (No. 62176187), the National Key Research and Development Program of China (No. 2017YFC1200500), the Research Foundation of Ministry of Education of China (No. 18JZD015), the Youth Fund for Humanities and Social Science Research of Ministry of Education of China (No. 22YJCZH064), the General Project of Natural Science Foundation of Hubei Province (No.2021CFB385).
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