# A Typological Examination of Modern Chinese OV Word Order Based on Deep Learning and the Design of Stance Expression Methods

Authors: ChangLin Wu; ChangAn Wu; Yue Zhang
Journal: Wireless Communications and Mobile Computing
 (2022)

---

## Abstract

Object-verb (OV) inflections are an important grammatical device in Chinese with denotative utility. The decorativeness of OV inflections shows different levels in Chinese: OV nouns denote things in reality and are the most denotative; OV independent structures can denote both denotation and trait; OV structures that are definite are not self-sufficient and denote a certain trait. The verb category of V is worn out in denotative OV structures, and OV phrases must repair the wear and tear of V’s category and enhance V’s declarativity when forming small sentences. In this paper, we propose an attention-based approach to Chinese stance representation based on modern Chinese OV order types; firstly, we use bidirectional (bidirectional) long and short-term memory neural networks (LSTM) and convolutional neural networks (CNNs) to obtain text representation vectors and local convolutional features, respectively, and then, we use attention mechanisms to add influence weight information to the local convolutional features and finally fuse the two features for classification. Experiments on the relevant corpus show that the method achieves better stance representation results, and the addition of attention mechanisms can effectively improve the accuracy of stance representation.


---

## Body

## 1. Introduction

Prosodic types are an important branch of contemporary language typology and a more well-studied area of contemporary language typology research. Natural human languages have three grammatically most important components that are seen as determining the basic order of language: subject (S), object (O), and predicate verb (V). Although there are also a few languages that are not subject-object languages, they can be measured with reference to this method. For example, in the tongue language, the S can be regarded as S, while the O can be regarded as O (there are two syntactic-semantic roles within the tongue: one is the subject of the intransitive verb, called “the subject,” and the other is the object of the transitive verb, typically “the subject”) [1–3]. The order of these three grammatical components determines the basic order of a language, which in turn determines a series of other related grammatical phenomena. A certain grammatical phenomenon occurs only in a particular type of language and not or rarely in another type of language. In other words, a particular basic sequential type contains a set of related grammatical phenomena. This is the commonality of inflectional order implication discussed in inflectional order typology. This is the reason why linguistic typologists study basic sequential types. Logically, there are six possible arrangements of these three grammatical components: SOV, SVO, VSO, VOS, OVS, and OSV, but interestingly, according to the results of a survey of 1377 languages, many of these languages belong to the first two basic orderings; the third, VSO, is in the minority, and the last three are in the absolute minority. There are 189 of these 1377 languages that do not have a clear tendency of order, and if we do statistical processing, we can exclude these 189 languages, so that we get 1188 languages’ order type distribution data, as shown in Table 1.Table 1
Distribution of sequential types in 1188 languages.
Sequence typeQuantityPercentageSOV56547.55%SVO48841.07%VSO957.99%VOS252.1%OVS110.92%OSV40.33%Total1188100%Why the basic order of human languages is concentrated on the first two orders (88.62%) can be explained by the cognitive processing level of the information structure. Both languages are S-first languages, and the S of human sentences is clearly oriented towards known information, while new information is mainly provided by the object. According to the “known-unknown” (or “old information-new information”) structural principle of the information structure, it is logical to place S in front of O. This can also be explained by VSO (VSO). This can also be seen by comparing the number of VSO (95 languages) and VOS (25 languages). In this way, the tendency of S to be arranged in front of O explains the first four languages of the order. Finally, looking at the comparison of the two inflectional orders OVS (11 species) and OSV (4 species), the common denominator is that S is behind O, but they show numerical superiority and inferiority: OVS is more numerous than OSV. The object is an intralingual element of the verb, which requires proximity to the verb; an object far from the verb is not a dominant grouping form. The object of OVS is close to the verb, while the object of OSV is far from the verb. Therefore, the former has more than the latter (Lu BF 2004). Among the above six inflectional arrangements, there are 1089 (91.66%) in which the object is close to the verb, while there are only 99 (8.3%) in which the object is not close to the verb [4–7].OV order is an important grammatical device in Chinese, and OV order shows a strong tendency to denotation. OV-ordered passive sentences account for a large proportion of passive sentences in Chinese. Use of order to express passivity is a common phenomenon in Chinese, and unmarked passive sentences use OV order to express passivity. Sentences in which the subject is the subject of the matter without the word are much more common in Chinese than passive sentences. The above statistics do not include OSV structured passive sentences, but even so, the number of OV structured passive sentences is enough to draw our attention, and the existence of many OV-ordered passive sentences in Chinese is a linguistic phenomenon that cannot be ignored [8–10]. Deep learning techniques have been popularly used in the analysis of Chinese languages and also in the development of the framework for Chinese language education. In the study in [11], the need for Chinese word segmentation is analyzed using deep learning-based Chinese natural language processing. The neural word-based models were benchmarked which depend on word segmentation against the neural character-based models. It does not include word segmentation in the four end-to-end NLP benchmark tasks, namely, language modeling, machine translation, sentence matching or phrase matching, and text classification. It was identified that character-based models worked far superior to word-based models. The study in [12] focused on the feasibility of using deep learning techniques in Chinese word segmentation and POS tagging. The study excluded the use of task-based feature engineering and used deep layers of neural networks to identify the most significant features in the tasks. A large-scale unlabeled dataset was used for Chinese character representation, and these were used to further improve the supervised word segmentation and POS word tagging models.In this paper, we propose an attention-based BiLSTM-CNN hybrid network model for modern Chinese OV inflection types, in which the intermediate text representation obtained from the improved BiLSTM proposed for the English stance expression task is used as part of the input data combined with the attention approach in the CNN pooling layer, and by calculating the local convolutional features and by calculating the cosine similarity between the local convolutional features and the intermediate text representation generated by BiLSTM, the influence weights are generated for the local convolutional features to achieve the purpose of highlighting the effective features, and finally, the sentence representation obtained by CNN learning and the intermediate text representation generated by BiLSTM are classified together by SoftMax to obtain the stance labels [13]. The main contribution of this paper is to apply the attention mechanism to the field of Chinese microblog stance representation. Experiments on the NLPCC corpus show that the method in this paper achieves better results, and the accuracy of stance expressions can be effectively improved by adding the attention mechanism. The types of modern Chinese language order are shown in Figure 1.Figure 1
Modern Chinese sequence types.The organization of the paper is as follows: Section2 presents a review of the related studies; Section 3 describes the methodologies followed by the experimentation results in Section 4. Section 5 finally consolidates the work done in the paper as a conclusion.


## 2. Related Work

### 2.1. Types of Chinese Sequences

The main reason for the controversy over the scope of “passive sentences” in Chinese is that the criteria for determining the scope of “passive sentences” are different: from the point of view of meaning, the subject statement has a passive meaning; from the point of view of form, there is no passive marker in the subject statement. The denationalization utility of Chinese OV sequencing devices is shown in Figure2. From the perspective of syntactic form, marked is opposed to unmarked; from the perspective of semantics, passive is opposed to active [14]. It is natural to have different views when judging subject-principal utterances by the criterion of meaning and by the criterion of form. In fact, the order is also a form of marking, but the degree of marking of the order is lower than that of the passive marker, which is easily ignored. The use of formal criteria to determine the scope of “passive sentences” is mainly influenced by the study of Indo-European grammar, where the morphological system is more developed, so researchers have paid special attention to formal marking. In contrast, Chinese has no morphological changes, and syntactically, the combination of Chinese is a “magnetic” adsorption rather than a morphological grafting. It is not enough to judge passive sentences based on formal markers alone. The concept of “passive” is the opposite of “active,” and we should not ignore this semantic feature but only combine syntactic and semantic features to make a more accurate judgment.Figure 2
Chinese OV denotative utility of sequential devices.VO and OV sequences show relative grammatical functions. Chinese VO-ordered sentences usually indicate the active, but do OV-ordered sentences have the function of indicating the passive? Before starting the discussion, a brief explanation of the relevant issues is needed. The issue of “passive” is a hot issue in Chinese grammar research, and there are many research results on the issue of “passive” in Chinese studies. However, there is still a controversy about the scope of “passive sentences” in Chinese. “Passive” and “passive sentence” are two different concepts, so the relationship between “passive” and “passive sentence” must be clarified. “Passive” is the opposite concept of “active” and belongs to the semantic category, which is characterized by “nonautonomy,” “nonwillfulness,” and “nonactivity.” The notion of passivity is a judgment based on the state of being experienced or formed by an object (the subject) under the control of a subject (the giver) and is based on the ability of human thinking and cognition to bring out objects and to draw causal inferences. The concept of passivity is widespread in many languages and is inseparable from human cognitive thinking. The term “passive sentence” belongs to the category of syntax, which refers to a sentence that expresses a passive relationship. The term “passive sentence” was first introduced in the study of Indo-European grammar, and a sentence in which the act described by the predicate is performed on the active person is called “passive.” The term “passive” is explained as “a term for the grammatical analysis of the tense, a form of sentence, clause, or verb in which the grammatical subject is generally the recipient or ‘target’ of the action represented by the verb. It is opposed to the active tense and sometimes to other forms such as the ‘middle’ (as in Greek).”The definition of “passive” is obviously based on Indo-European languages, where the scope of passive sentences is generally clear. In Chinese, there is a “passive concept,” which is syntactically expressed as a sentence expressing passive meaning, but the scope of passive sentences in Chinese is controversial. Although there are different views on the scope of passive sentences, the first question we would like to discuss in this paper is whether the OV sequential structure expresses passive meaning, and then, we would like to consider whether the OV sequential structure is a passive sentence. First, from the perspective of ephemerality, Chinese sentences can express passive meaning without passive markers. The passive tense did not exist in primitive Chinese, but verbs used for passive meaning existed in Upper Ancient Chinese and were used until after the Han Dynasty. Wang Li believes that the form of such sentences is the same as that of active sentences and cannot be regarded as passive. Regardless of whether such sentences are passive or not, it is certain that they can express passive meanings. Since ancient times, Chinese has been able to make active-passive conversions, which are influenced by the mechanism of “giving and receiving the same word,” and the same verb can express both active and passive meanings. From the perspective of VO order versus OV order, VO-ordered sentences in Chinese usually express active meanings, while OV-ordered sentences usually express passive meanings; OV order is a grammatical means of expressing passivity in Chinese, and OV order, as a grammatical means of Chinese, has the effect of passivation form. The syntactic-semantic criterion is commonly used in typological studies, so I combine both syntactic and semantic features and call all sentences in which OV order represents passive relations “passive sentences.” According to the presence or absence of passive markers, I classify passive sentences into marked passive sentences and OV-ordered passive sentences, the latter including “O+V” and “O+S+V” structures. For the sake of brevity, the “O+V” and “O+S+V” constructions are abbreviated as OV and OSV constructions, respectively, in the following presentation. Since the passive markers “be” and “call,” “teach,” “let,” “give,” etc., are mainly written words with the same meaning which are mainly the difference between written and spoken language, I will use the “bei” sentence as a representative sentence type to illustrate the case of marked passive sentences in the following discussion. It is worth noting that OSV passive sentences are sometimes called “subject-verb-predicate sentences” in Chinese, and these sentences are influenced by topic-oriented factors, and the passive meaning of the expression is not obvious, but the passive meaning of the expression cannot be denied [14].


### 2.2. Expression Method of Position

With the vigorous development of Web 2.0, the Internet gradually advocates user-centeredness and moves from passively receiving Internet information to actively creating Internet information. As a result, the Internet has generated a large number of user-participated comments on valuable information such as people and events [15–17]. These comments express various emotional colors and tendencies of people’s positions, such as criticism and praise. With the rapid expansion of the scale of social media information, it is difficult to cope with the collection and processing of the massive amount of information online by manual methods alone, so there is an urgent need for computers to help users quickly access and organize this relevant evaluation information and to explore the hidden social phenomena contained in it. The structure of the field representation is shown in Figure 3.Figure 3
Structure diagram of standpoint expression.The stance expression task has received more and more attention from researchers, while the stance expression task on Chinese microblogs was also presented at the Natural Language Processing and Chinese Computing Conference in 2016. It is enough to see the urgent need for stance information detection based on social texts. The stance expression task hopes to mine users’ stance information from user-generated texts. The stance information is the emotional tendency of a description subject, which can be a product, an event, or a person. In existing studies, stance expression is generally abstracted as a classification problem. The position expression problem needs to explore the position information embedded in the text: for, against, and no position, respectively. Similar to the stance expression problem, the sentiment analysis problem is also directed at analyzing textual tendencies. The main difference between the two is that position tendency is generated for a certain descriptive subject, while sentiment analysis focuses on positive or negative emotional features in the text, such as whether it contains words with strong emotional messages and does not involve the descriptive subject as information. Currently, researchers have proposed many proven research methods in the field of stance representation, the vast majority of which use a combination of feature engineering and traditional machine learning [18, 19].Researchers mine the stance information in texts by manually mining knowledge in the domain, manually constructing task-related semantic features, adding domain-related lexical resources, etc. To ensure the quality of features, traditional text feature design requires a large amount of expert domain knowledge and high labor cost, and the effectiveness of using feature engineering and traditional machine learning modeling methods depends heavily on feature selection strategies and tuning of model parameters. Based on the above inherent drawbacks of feature engineering methods, researchers purposefully propose to apply deep learning methods to the field of stance representation to efficiently extract valuable features. Deep learning uses neural network models to automatically learn feature representations that can describe the essence of the data, transforming the original data into higher-level abstract representations by some simple nonlinear models and then combining multiple layers of transformations to learn complex functional features. Compared with traditional machine learning, deep learning avoids the limitation of manual feature design and reduces the huge cost of manual feature design by self-learning feature mechanisms, and deep learning models also have better generalization and portability. In the existing research work on the application of deep learning for stance representation, researchers have not considered that different features have different influences on the overall stance information of the sentence. At the same time, Chinese microblogging texts are naturally characterized by sparse information. Chinese microblog texts are usually only 1-140 words long and often contain short links, emoticons, and other informal texts, so the information contained in a single entry is very limited. By analyzing specific examples, we found that a few key words in Chinese microblogs can roughly reflect the position category of the sentence. For example, “of” and “with” are not useful for expressing the position of a sentence, while “likes,” “benefits,” and “bad comments” are not useful for expressing the position of a sentence, and “bad comment” can well reflect the stance of the sentence [20–22]. Therefore, limited by the sparsity and irregularity of Chinese microblogging text and considering the idea that researchers do not differentiate the importance of features but treat them equally in existence using deep learning methods, how to obtain a richer representation of text features from limited sentence information is the problem to be solved in this paper [23]. CNNs have been proven to have powerful feature extraction capability in text modeling. The main purpose of pooling operation in CNN is to effectively reduce the parameters in the model and fix the dimensionality of the output features, which is an essential step in CNN. The commonly used pooling strategies are divided into two types: maximum pooling and average pooling. Although maximum pooling can reduce the number of model parameters and help alleviate the model overfitting problem, it loses the order information of text and the intensity information of features [24]. The average pooling strategy averages the features in the domain and loses the intensity information of the features. Thus, this paper draws on the idea of the attention mechanism to improve the pooling strategy by calculating the influence weights of different features to measure the importance of features, which can highlight the important convolutional features and improve the inherent problems of the traditional pooling strategy [25, 26]. Emotion processing of vocabulary has become extremely popular in recent days wherein emotion analysis mining has become extremely predominant. In the case of Chinese word sentiment extraction and word segmentation, operation is extremely tedious as it involves the inclusion of multiple implicit and explicit words. The study in [27] uses expressions and sentiment vocabulary dictionaries in combination with hybrid structures to develop information synergy methods that enable sentimental analysis. The study in [28] discusses the role of an intelligent teaching system which involves the use of network learning and network intelligent teaching platforms. The use of deep learning further enhances the ITS framework enabling teaching based on student aptitude and reflecting the student actual learning state and characteristics. The study analyzes the influencing factors involved in the student learning process and helps in developing an optimized ITS.


## 2.1. Types of Chinese Sequences

The main reason for the controversy over the scope of “passive sentences” in Chinese is that the criteria for determining the scope of “passive sentences” are different: from the point of view of meaning, the subject statement has a passive meaning; from the point of view of form, there is no passive marker in the subject statement. The denationalization utility of Chinese OV sequencing devices is shown in Figure2. From the perspective of syntactic form, marked is opposed to unmarked; from the perspective of semantics, passive is opposed to active [14]. It is natural to have different views when judging subject-principal utterances by the criterion of meaning and by the criterion of form. In fact, the order is also a form of marking, but the degree of marking of the order is lower than that of the passive marker, which is easily ignored. The use of formal criteria to determine the scope of “passive sentences” is mainly influenced by the study of Indo-European grammar, where the morphological system is more developed, so researchers have paid special attention to formal marking. In contrast, Chinese has no morphological changes, and syntactically, the combination of Chinese is a “magnetic” adsorption rather than a morphological grafting. It is not enough to judge passive sentences based on formal markers alone. The concept of “passive” is the opposite of “active,” and we should not ignore this semantic feature but only combine syntactic and semantic features to make a more accurate judgment.Figure 2
Chinese OV denotative utility of sequential devices.VO and OV sequences show relative grammatical functions. Chinese VO-ordered sentences usually indicate the active, but do OV-ordered sentences have the function of indicating the passive? Before starting the discussion, a brief explanation of the relevant issues is needed. The issue of “passive” is a hot issue in Chinese grammar research, and there are many research results on the issue of “passive” in Chinese studies. However, there is still a controversy about the scope of “passive sentences” in Chinese. “Passive” and “passive sentence” are two different concepts, so the relationship between “passive” and “passive sentence” must be clarified. “Passive” is the opposite concept of “active” and belongs to the semantic category, which is characterized by “nonautonomy,” “nonwillfulness,” and “nonactivity.” The notion of passivity is a judgment based on the state of being experienced or formed by an object (the subject) under the control of a subject (the giver) and is based on the ability of human thinking and cognition to bring out objects and to draw causal inferences. The concept of passivity is widespread in many languages and is inseparable from human cognitive thinking. The term “passive sentence” belongs to the category of syntax, which refers to a sentence that expresses a passive relationship. The term “passive sentence” was first introduced in the study of Indo-European grammar, and a sentence in which the act described by the predicate is performed on the active person is called “passive.” The term “passive” is explained as “a term for the grammatical analysis of the tense, a form of sentence, clause, or verb in which the grammatical subject is generally the recipient or ‘target’ of the action represented by the verb. It is opposed to the active tense and sometimes to other forms such as the ‘middle’ (as in Greek).”The definition of “passive” is obviously based on Indo-European languages, where the scope of passive sentences is generally clear. In Chinese, there is a “passive concept,” which is syntactically expressed as a sentence expressing passive meaning, but the scope of passive sentences in Chinese is controversial. Although there are different views on the scope of passive sentences, the first question we would like to discuss in this paper is whether the OV sequential structure expresses passive meaning, and then, we would like to consider whether the OV sequential structure is a passive sentence. First, from the perspective of ephemerality, Chinese sentences can express passive meaning without passive markers. The passive tense did not exist in primitive Chinese, but verbs used for passive meaning existed in Upper Ancient Chinese and were used until after the Han Dynasty. Wang Li believes that the form of such sentences is the same as that of active sentences and cannot be regarded as passive. Regardless of whether such sentences are passive or not, it is certain that they can express passive meanings. Since ancient times, Chinese has been able to make active-passive conversions, which are influenced by the mechanism of “giving and receiving the same word,” and the same verb can express both active and passive meanings. From the perspective of VO order versus OV order, VO-ordered sentences in Chinese usually express active meanings, while OV-ordered sentences usually express passive meanings; OV order is a grammatical means of expressing passivity in Chinese, and OV order, as a grammatical means of Chinese, has the effect of passivation form. The syntactic-semantic criterion is commonly used in typological studies, so I combine both syntactic and semantic features and call all sentences in which OV order represents passive relations “passive sentences.” According to the presence or absence of passive markers, I classify passive sentences into marked passive sentences and OV-ordered passive sentences, the latter including “O+V” and “O+S+V” structures. For the sake of brevity, the “O+V” and “O+S+V” constructions are abbreviated as OV and OSV constructions, respectively, in the following presentation. Since the passive markers “be” and “call,” “teach,” “let,” “give,” etc., are mainly written words with the same meaning which are mainly the difference between written and spoken language, I will use the “bei” sentence as a representative sentence type to illustrate the case of marked passive sentences in the following discussion. It is worth noting that OSV passive sentences are sometimes called “subject-verb-predicate sentences” in Chinese, and these sentences are influenced by topic-oriented factors, and the passive meaning of the expression is not obvious, but the passive meaning of the expression cannot be denied [14].


## 2.2. Expression Method of Position

With the vigorous development of Web 2.0, the Internet gradually advocates user-centeredness and moves from passively receiving Internet information to actively creating Internet information. As a result, the Internet has generated a large number of user-participated comments on valuable information such as people and events [15–17]. These comments express various emotional colors and tendencies of people’s positions, such as criticism and praise. With the rapid expansion of the scale of social media information, it is difficult to cope with the collection and processing of the massive amount of information online by manual methods alone, so there is an urgent need for computers to help users quickly access and organize this relevant evaluation information and to explore the hidden social phenomena contained in it. The structure of the field representation is shown in Figure 3.Figure 3
Structure diagram of standpoint expression.The stance expression task has received more and more attention from researchers, while the stance expression task on Chinese microblogs was also presented at the Natural Language Processing and Chinese Computing Conference in 2016. It is enough to see the urgent need for stance information detection based on social texts. The stance expression task hopes to mine users’ stance information from user-generated texts. The stance information is the emotional tendency of a description subject, which can be a product, an event, or a person. In existing studies, stance expression is generally abstracted as a classification problem. The position expression problem needs to explore the position information embedded in the text: for, against, and no position, respectively. Similar to the stance expression problem, the sentiment analysis problem is also directed at analyzing textual tendencies. The main difference between the two is that position tendency is generated for a certain descriptive subject, while sentiment analysis focuses on positive or negative emotional features in the text, such as whether it contains words with strong emotional messages and does not involve the descriptive subject as information. Currently, researchers have proposed many proven research methods in the field of stance representation, the vast majority of which use a combination of feature engineering and traditional machine learning [18, 19].Researchers mine the stance information in texts by manually mining knowledge in the domain, manually constructing task-related semantic features, adding domain-related lexical resources, etc. To ensure the quality of features, traditional text feature design requires a large amount of expert domain knowledge and high labor cost, and the effectiveness of using feature engineering and traditional machine learning modeling methods depends heavily on feature selection strategies and tuning of model parameters. Based on the above inherent drawbacks of feature engineering methods, researchers purposefully propose to apply deep learning methods to the field of stance representation to efficiently extract valuable features. Deep learning uses neural network models to automatically learn feature representations that can describe the essence of the data, transforming the original data into higher-level abstract representations by some simple nonlinear models and then combining multiple layers of transformations to learn complex functional features. Compared with traditional machine learning, deep learning avoids the limitation of manual feature design and reduces the huge cost of manual feature design by self-learning feature mechanisms, and deep learning models also have better generalization and portability. In the existing research work on the application of deep learning for stance representation, researchers have not considered that different features have different influences on the overall stance information of the sentence. At the same time, Chinese microblogging texts are naturally characterized by sparse information. Chinese microblog texts are usually only 1-140 words long and often contain short links, emoticons, and other informal texts, so the information contained in a single entry is very limited. By analyzing specific examples, we found that a few key words in Chinese microblogs can roughly reflect the position category of the sentence. For example, “of” and “with” are not useful for expressing the position of a sentence, while “likes,” “benefits,” and “bad comments” are not useful for expressing the position of a sentence, and “bad comment” can well reflect the stance of the sentence [20–22]. Therefore, limited by the sparsity and irregularity of Chinese microblogging text and considering the idea that researchers do not differentiate the importance of features but treat them equally in existence using deep learning methods, how to obtain a richer representation of text features from limited sentence information is the problem to be solved in this paper [23]. CNNs have been proven to have powerful feature extraction capability in text modeling. The main purpose of pooling operation in CNN is to effectively reduce the parameters in the model and fix the dimensionality of the output features, which is an essential step in CNN. The commonly used pooling strategies are divided into two types: maximum pooling and average pooling. Although maximum pooling can reduce the number of model parameters and help alleviate the model overfitting problem, it loses the order information of text and the intensity information of features [24]. The average pooling strategy averages the features in the domain and loses the intensity information of the features. Thus, this paper draws on the idea of the attention mechanism to improve the pooling strategy by calculating the influence weights of different features to measure the importance of features, which can highlight the important convolutional features and improve the inherent problems of the traditional pooling strategy [25, 26]. Emotion processing of vocabulary has become extremely popular in recent days wherein emotion analysis mining has become extremely predominant. In the case of Chinese word sentiment extraction and word segmentation, operation is extremely tedious as it involves the inclusion of multiple implicit and explicit words. The study in [27] uses expressions and sentiment vocabulary dictionaries in combination with hybrid structures to develop information synergy methods that enable sentimental analysis. The study in [28] discusses the role of an intelligent teaching system which involves the use of network learning and network intelligent teaching platforms. The use of deep learning further enhances the ITS framework enabling teaching based on student aptitude and reflecting the student actual learning state and characteristics. The study analyzes the influencing factors involved in the student learning process and helps in developing an optimized ITS.


## 3. Methods

### 3.1. Model Structure

In this paper, an attention-based BiLSTM-CNN hybrid network stance representation model is proposed. The use of BiLSTM-CNN has various advantages in solving real-world problems especially in the case of natural language processing. In the case of BiLSTM-CNN, all the components of an input sequence hold information from both the past and the present which enables the model to generate meaningful output. This is achieved due to the combination of LSTM layers from both directions. This model is extremely useful in performing multiple NLP tasks, namely, sentence classification, translation, and entity recognition, and has applications in speech recognition, prediction of protein structures, handwriting recognition, and various other sectors. As an example, a BiLSTM-CNN-CRF-based deep learning model was developed for Mongolian word segmentation in [29]. The study used a CNN algorithm in a bidirectional long-short-term memory network to analyze the local features of Mongolian character sequences. The Mongolian word segmentation was enhanced by extracting the local features and time-dependent long-range features, and then, a conditional random field approach was implemented for constrained correction. The present paper uses word2vec to obtain the word vector representation of the text. Then, the word vectors are input to BiLSTM and CNN, respectively, to obtain intermediate text representations and convolutional features. In order to alleviate the information loss problem of the CNN pooling layer and further optimize the convolutional features, the attention-based pooling strategy is applied to the model in this paper, and the intermediate text representation obtained by using BiLSTM learning is used to guide the weighting of convolutional features. Finally, the final text representation obtained by CNN learning and the intermediate text representation obtained by BiLSTM are stitched together and fed into the SoftMax layer together to obtain three types of stance labels. The specific implementation of each part of the model is described next. The model structure diagram is shown in Figure 4.Figure 4
Model structure diagram.


### 3.2. Word Vector Representation

In this paper, we use open-source tools to obtain the textual word vector representation. In this paper, the skip-gram algorithm is used to obtain the word vector representation of Chinese microblogs. In practical applications, the size of the corpus required for training, the dimensionality of the word vector, and other factors will have an impact on the quality of the word vector. In this paper, in addition to using the corpus provided by NLPCC, we also add 10 million Chinese microblogs collected together for word vector training. The obtained descriptive subject word vector representations and descriptive text (Chinese microblogs) word vector representations are used as the input data for BiLSTM. The descriptive text word vector representation is used as the input data for CNN. Since using CNN to process natural language problems, it is necessary to convert the sentences into numerical representations in matrix form as the input data. In this paper, the matrix numerical representation of the sentence is obtained by stitching the word vector representation of each word in the sentence. Assuming that the length of the Chinese text of a Chinese microblog isn and the word vector dimension is d, a matrix X of dimension n×d is obtained by stacking the word vectors of each word in the sentence as the input data of the CNN.


### 3.3. Bidirectional LSTM

In the part of the implementation of the BiLSTM module in the attention-based BiLSTM-CNN hybrid network model, this paper uses a modified BiLSTM model, which has obtained very good results in the stance expression task for English Twitter released by Semeval, and the specific implementation of this model is described below. The BiLSTM structure is shown in Figure5. Since the recurrent neural network based on LSTM units can effectively use the historical tagging information, it can be considered that the description subject has some guiding significance for the description text, so two LSTM models are built by training the LSTM target for the description subject and the LSTM-Weibo for the description text. The final implied unit state output from the target is used as the initial state of the LSTM-Weibo implied unit. We describe the process of model state transfer. Sequences w1,w2 denote description subjects, sequences w3,w4,⋯,w7 denote description texts, and C denotes the BiLSTM final output of the intermediate text representation vector.Figure 5
BiLSTM structure diagram.The information describing the subject in the LSTM-Weibo model is enhanced by the transfer of states. The state transfer process of the one-way LSTM is given in the following equation, where the sequencew1 represents the description subject, the sequence wT+1 represents the description text, and the initial state of h0,c0 is 0:
(1)h1,c1=LSTMTargetw1,h0,c0,hT,cT=LSTMTargetwT,hT−1,cT−1,hT+1,cT+1=LSTMWeibowT+1,h0,cT,hN,cN=LSTMWeibowN,hN−1,cN−1.BiLSTM will eventually produce two output vectorshr and hl in two directions and connect them sequentially to obtain the intermediate text representation vector C∈R1×2H, where H denotes the predefined number of implied units:
(2)C=hr⊕hl.


### 3.4. Feature Extraction

The convolutional layer receives a Chinese microblogging word vector feature matrixX of size n×d. The matrix contains n words, and the word vector dimension is d. Each row of the matrix X is a word vector of one word in the sentence. A convolution kernel w is selected to convolve the input matrix X to obtain the eigenvalues gi. In this paper, the “same” mode is used for convolution, which will obtain the same output as the input matrix size, and the convolution process is shown in
(3)gi=fw×Xi:i−m+1+b,where f denotes the ReLu (Rectified Linear Units) activation function. To speed up the training convergence, the activation function used here is the ReLu function. m is the size of the sliding window for the convolution calculation, and b denotes the bias to term. Xi:i−m+1 denotes the local features extracted in the range of X. In this paper, the extracted results of the convolutional kernel are composed of an n×T feature matrix S:
(4)S=v1,v2,⋯,vn=g11g12⋯g1Tg21g22⋯g2T⋮⋮⋮gn1gn2⋯gnT∈Rn×T,where T denotes the number of convolutional kernels and n denotes the sentence length. Each column of the matrix S is the feature vector obtained after the convolution operation:
(5)G=g1,g2,⋯,gn∈Rn.Each row in the matrix represents the extraction results ofT convolutional kernels at the same position in the sentence matrix. Since the extraction results of T convolutional kernels are pooled, the row vector vi in S represents all the convolutional features extracted for a given position of the sentence.


### 3.5. Pooling Mechanism

Since Chinese microblogs have the characteristics of short text and sparse information and the traditional pooling strategy generates the problem of information loss, how to learn a richer text representation from Chinese microblogs is the focus of this paper. The attention mechanism of neural networks is based on the “attention” principle in the human visual system, by focusing on the most relevant parts of the target object (e.g., image and text) instead of all information. The main idea is that different words in a sentence contain different semantic information, and the words containing more semantic information will have a higher influence weight, so that the attention mechanism can determine which words in a sentence are relatively important and achieve the purpose of optimizing the model. Therefore, this paper purposefully adopts the attention mechanism-based pooling strategy, which not only makes the important features highlighted but also alleviates the information loss problem caused by the traditional pooling strategy. And the improved BiLSTM model obtained in the previous step is used as the input data of the attention pooling mechanism. Since BiLSTM can well preserve the historical, future information, and temporal information of the text, the intermediate text representation vector generated by the improved BiLSTM model is a high-quality representation of the original text and can represent the overall information of the sentence. Compared with BiLSTM, CNN can better extract the local contextual information of the sentence. Therefore, this paper measures the contribution of the local convolutional features to the overall semantic information of the sentence by calculating the cosine similarity between the intermediate text representation and the local convolutional features:(6)ei=cossimC,vi,ai=expei∑i=1nexpei.After calculating the attention weights of all the local convolutional features, the attention weight matrix is obtained, and the local convolutional featuresvi points are multiplied by the corresponding attention weight value ai one by one to complete the weighting. Finally, the cumulative operation is performed on the weighted local convolutional features to obtain the final sentence representation R of CNN, which is calculated as shown in
(7)R=∑i=1naivi∈R1×T.


### 3.6. SoftMax Layer

The final sentence representationR of CNN relates to the intermediate text representation C generated by BiLSTM as the final stance expression feature vector, and the stance expression determination result is output using SoftMax. The SoftMax layer allots decimal probabilities to each of the classes in a multiclass problem, and all of these decimal probabilities add up to 1. This additional constraint helps in the convergence of the training much faster than the traditional approaches. Based on the sample labels in the training data, a backpropagation algorithm is used to perform gradient updates on the model parameters. Backpropagation helps in minimizing the cost function of the model by adjusting the network weights and biases. These adjustments are determined using the gradients of the cost function with respect to the concerned parameters. The gradients help in optimizing the model’s parameters. The classification process is shown in
(8)y=wsC⊕R⊙p+bsprobc=expyc∑c′∈Lexpyc′.


## 3.1. Model Structure

In this paper, an attention-based BiLSTM-CNN hybrid network stance representation model is proposed. The use of BiLSTM-CNN has various advantages in solving real-world problems especially in the case of natural language processing. In the case of BiLSTM-CNN, all the components of an input sequence hold information from both the past and the present which enables the model to generate meaningful output. This is achieved due to the combination of LSTM layers from both directions. This model is extremely useful in performing multiple NLP tasks, namely, sentence classification, translation, and entity recognition, and has applications in speech recognition, prediction of protein structures, handwriting recognition, and various other sectors. As an example, a BiLSTM-CNN-CRF-based deep learning model was developed for Mongolian word segmentation in [29]. The study used a CNN algorithm in a bidirectional long-short-term memory network to analyze the local features of Mongolian character sequences. The Mongolian word segmentation was enhanced by extracting the local features and time-dependent long-range features, and then, a conditional random field approach was implemented for constrained correction. The present paper uses word2vec to obtain the word vector representation of the text. Then, the word vectors are input to BiLSTM and CNN, respectively, to obtain intermediate text representations and convolutional features. In order to alleviate the information loss problem of the CNN pooling layer and further optimize the convolutional features, the attention-based pooling strategy is applied to the model in this paper, and the intermediate text representation obtained by using BiLSTM learning is used to guide the weighting of convolutional features. Finally, the final text representation obtained by CNN learning and the intermediate text representation obtained by BiLSTM are stitched together and fed into the SoftMax layer together to obtain three types of stance labels. The specific implementation of each part of the model is described next. The model structure diagram is shown in Figure 4.Figure 4
Model structure diagram.


## 3.2. Word Vector Representation

In this paper, we use open-source tools to obtain the textual word vector representation. In this paper, the skip-gram algorithm is used to obtain the word vector representation of Chinese microblogs. In practical applications, the size of the corpus required for training, the dimensionality of the word vector, and other factors will have an impact on the quality of the word vector. In this paper, in addition to using the corpus provided by NLPCC, we also add 10 million Chinese microblogs collected together for word vector training. The obtained descriptive subject word vector representations and descriptive text (Chinese microblogs) word vector representations are used as the input data for BiLSTM. The descriptive text word vector representation is used as the input data for CNN. Since using CNN to process natural language problems, it is necessary to convert the sentences into numerical representations in matrix form as the input data. In this paper, the matrix numerical representation of the sentence is obtained by stitching the word vector representation of each word in the sentence. Assuming that the length of the Chinese text of a Chinese microblog isn and the word vector dimension is d, a matrix X of dimension n×d is obtained by stacking the word vectors of each word in the sentence as the input data of the CNN.


## 3.3. Bidirectional LSTM

In the part of the implementation of the BiLSTM module in the attention-based BiLSTM-CNN hybrid network model, this paper uses a modified BiLSTM model, which has obtained very good results in the stance expression task for English Twitter released by Semeval, and the specific implementation of this model is described below. The BiLSTM structure is shown in Figure5. Since the recurrent neural network based on LSTM units can effectively use the historical tagging information, it can be considered that the description subject has some guiding significance for the description text, so two LSTM models are built by training the LSTM target for the description subject and the LSTM-Weibo for the description text. The final implied unit state output from the target is used as the initial state of the LSTM-Weibo implied unit. We describe the process of model state transfer. Sequences w1,w2 denote description subjects, sequences w3,w4,⋯,w7 denote description texts, and C denotes the BiLSTM final output of the intermediate text representation vector.Figure 5
BiLSTM structure diagram.The information describing the subject in the LSTM-Weibo model is enhanced by the transfer of states. The state transfer process of the one-way LSTM is given in the following equation, where the sequencew1 represents the description subject, the sequence wT+1 represents the description text, and the initial state of h0,c0 is 0:
(1)h1,c1=LSTMTargetw1,h0,c0,hT,cT=LSTMTargetwT,hT−1,cT−1,hT+1,cT+1=LSTMWeibowT+1,h0,cT,hN,cN=LSTMWeibowN,hN−1,cN−1.BiLSTM will eventually produce two output vectorshr and hl in two directions and connect them sequentially to obtain the intermediate text representation vector C∈R1×2H, where H denotes the predefined number of implied units:
(2)C=hr⊕hl.


## 3.4. Feature Extraction

The convolutional layer receives a Chinese microblogging word vector feature matrixX of size n×d. The matrix contains n words, and the word vector dimension is d. Each row of the matrix X is a word vector of one word in the sentence. A convolution kernel w is selected to convolve the input matrix X to obtain the eigenvalues gi. In this paper, the “same” mode is used for convolution, which will obtain the same output as the input matrix size, and the convolution process is shown in
(3)gi=fw×Xi:i−m+1+b,where f denotes the ReLu (Rectified Linear Units) activation function. To speed up the training convergence, the activation function used here is the ReLu function. m is the size of the sliding window for the convolution calculation, and b denotes the bias to term. Xi:i−m+1 denotes the local features extracted in the range of X. In this paper, the extracted results of the convolutional kernel are composed of an n×T feature matrix S:
(4)S=v1,v2,⋯,vn=g11g12⋯g1Tg21g22⋯g2T⋮⋮⋮gn1gn2⋯gnT∈Rn×T,where T denotes the number of convolutional kernels and n denotes the sentence length. Each column of the matrix S is the feature vector obtained after the convolution operation:
(5)G=g1,g2,⋯,gn∈Rn.Each row in the matrix represents the extraction results ofT convolutional kernels at the same position in the sentence matrix. Since the extraction results of T convolutional kernels are pooled, the row vector vi in S represents all the convolutional features extracted for a given position of the sentence.


## 3.5. Pooling Mechanism

Since Chinese microblogs have the characteristics of short text and sparse information and the traditional pooling strategy generates the problem of information loss, how to learn a richer text representation from Chinese microblogs is the focus of this paper. The attention mechanism of neural networks is based on the “attention” principle in the human visual system, by focusing on the most relevant parts of the target object (e.g., image and text) instead of all information. The main idea is that different words in a sentence contain different semantic information, and the words containing more semantic information will have a higher influence weight, so that the attention mechanism can determine which words in a sentence are relatively important and achieve the purpose of optimizing the model. Therefore, this paper purposefully adopts the attention mechanism-based pooling strategy, which not only makes the important features highlighted but also alleviates the information loss problem caused by the traditional pooling strategy. And the improved BiLSTM model obtained in the previous step is used as the input data of the attention pooling mechanism. Since BiLSTM can well preserve the historical, future information, and temporal information of the text, the intermediate text representation vector generated by the improved BiLSTM model is a high-quality representation of the original text and can represent the overall information of the sentence. Compared with BiLSTM, CNN can better extract the local contextual information of the sentence. Therefore, this paper measures the contribution of the local convolutional features to the overall semantic information of the sentence by calculating the cosine similarity between the intermediate text representation and the local convolutional features:(6)ei=cossimC,vi,ai=expei∑i=1nexpei.After calculating the attention weights of all the local convolutional features, the attention weight matrix is obtained, and the local convolutional featuresvi points are multiplied by the corresponding attention weight value ai one by one to complete the weighting. Finally, the cumulative operation is performed on the weighted local convolutional features to obtain the final sentence representation R of CNN, which is calculated as shown in
(7)R=∑i=1naivi∈R1×T.


## 3.6. SoftMax Layer

The final sentence representationR of CNN relates to the intermediate text representation C generated by BiLSTM as the final stance expression feature vector, and the stance expression determination result is output using SoftMax. The SoftMax layer allots decimal probabilities to each of the classes in a multiclass problem, and all of these decimal probabilities add up to 1. This additional constraint helps in the convergence of the training much faster than the traditional approaches. Based on the sample labels in the training data, a backpropagation algorithm is used to perform gradient updates on the model parameters. Backpropagation helps in minimizing the cost function of the model by adjusting the network weights and biases. These adjustments are determined using the gradients of the cost function with respect to the concerned parameters. The gradients help in optimizing the model’s parameters. The classification process is shown in
(8)y=wsC⊕R⊙p+bsprobc=expyc∑c′∈Lexpyc′.


## 4. Experiments and Results

### 4.1. Dataset

In this paper, the open corpus dataset provided by the Chinese microblogging stance expression task published by NLPCC is chosen to validate the method of this paper. The dataset provides 3000 training data with labeled stance categories and 1000 test data with known stance category labels. Each data contains a Chinese microblog body, description subject, and stance label. The agreed stance category labels in the dataset are FAVOR, AGAINST, and NONE, which indicate three stances of support, opposition, and no stance, respectively. The dataset is divided into five description subjects, which are #Chinese New Year firecrackers, #IphoneSe, #Russia’s anti-terrorist operation in Syria, #Opening the second child, and #Shenzhen’s motorbike ban.


### 4.2. Data Preprocessing

Chinese microblog texts often contain informal texts such as emojis, URLs, usernames, and the topic hashtag #Hashtag, which is unique to the Chinese microblog corpus. These informal texts pose a great challenge to the stance representation task. In order to remove unnecessary noise interference, this paper preprocesses the Chinese microblog text as follows: (1) Filter all punctuation and special characters and keep only the Chinese and English texts with semantic value information. (2) Format conversion. The full-corner English characters in the original data are converted to half-corner English characters. All English characters are converted to lowercase characters. (3) Remove short links and @ tags in Chinese microblogs. (4) Word separation processing. There is no direct distinction between words in Chinese, so the Chinese text needs to be divided into words. The effect of word separation will directly affect the experimental results. In this paper, we use jieba word separation for Chinese word separation.


### 4.3. Evaluation Indexes

The main evaluation metrics for evaluating classifiers are accuracy and recall. For a given test dataset, the accuracy rate is the ratio of the number of samples correctly classified by the classifier to the total number of samples, and the recall rate is the proportion of correctly determined positive cases to the total number of positive cases, and the two are mutually constrained. To balance the relationship between accuracy and recall, a comprehensive measureF-measure (F-measure) is introduced as a classifier evaluation metric. In the official evaluation criteria provided by NLPCC, the F-measure (FFAVOR) for the support (FAVOR) label and the F-measure (FAGAINST) for the opposition (AGAINST) label need to be calculated, and the macroaverage of the two (Favg) is used as the final evaluation metric. The loss convergence diagram of the training process is shown in Figure 6.Figure 6
Schematic diagram of the loss convergence of the training process.


### 4.4. Experimental Results

TheFavg values for different dropout ratios p and different numbers of filters T are given in Table 2. From the experimental results, when the dropout value p is greater than 0.5, the classification effect of the stance expression decreases significantly. When the number of filters T increases, Favg of stance expression improves, and it is best for stance expression when T reaches 150. When computing similarity, in order to map BiLSTM text representation and local convolutional features to the same dimension, we make T=2H, so the number of convolutional kernels T affects the complexity of the BiLSTM module. In summary, in this paper, under the premise of no significant increase in model training complexity and training time, the final number of 150 filters is chosen, and the dropout ratio p is 0.5 Under this condition, the accuracy of the stance representation method designed in this paper can reach 0.5791.Table 2
The effect of stance expression with a different number of filters and dropout ratio.
T=50T=80T=100T=150p=0.10.49710.50200.53370.5639p=0.30.49330.49730.53720.5701p=0.50.48900.49110.53900.5791p=0.70.46420.47030.49150.4972p=0.90.31970.33210.34800.3540In order to verify the effectiveness of the attention-based hybrid neural network proposed in this paper in the Chinese microblog stance expression method, the method in this paper is compared with traditional machine learning methods (SVM), CNN, BiLSTM, and BiLSTM-CNN hybrid network without added attention mechanism and baseline. The specific experiments are as follows: (1) Traditional machine learning: word2vec model is used to train word vectors as input data, and SVM is used for classification. (2) CNN: word2vec is used to train word vectors, and CNN is trained to extract features and classify. (3) BiLSTM: word2vec is used to train word vectors, and improved BiLSTM is trained to extract features and classify. (4) BiLSTM-CNN-max: word2vec trained word vectors are used, features are extracted using CNN based on 1-maxpooling strategy, and text representation learned by fusion BiLSTM is classified. (5) Nanyu-NN: hybrid neural network method proposed by Nanyu is used as a baseline. (6) In this paper, method BiLSTM-CNN-ATT: the attention-based BiLSTM-CNN hybrid neural network proposed in this paper is used for feature extraction and classification.A comparison of the performance of different methods is shown in Table3. (1) Comparison with baseline Nanyu-NN: the idea of Nanyu is to add LSTM between the convolutional and pooling layers of CNN to learn the text representation again and build a hybrid network model. By comparing with this paper’s method, we can see that this paper uses word2vec as the base word representation to build the BiLSTM-CNN hybrid network model and improves the pooling layer to optimize the features by the attention mechanism. The effect of this paper is 2.23% better than that of the hybrid network model proposed by Nanyu, which can effectively improve the performance of stance representation. (2) By comparing the single-structured CNN and BiLSTM models, it is found that the hybrid network model performs better than the single-structured neural network. Theoretically, as the depth of the model increases, the expressiveness of the model is better due to the increased parameters. However, the models are more complex and more prone to overfitting. In this paper, the balance is performed by dropout strategy and L2 regularity. (3) By comparing the BiLSTM-CNN model with the common pooling strategy without adding the attention mechanism, it is found that the attention mechanism can effectively optimize the features and bring out the important features. The pooling layer mitigates the information loss problem through the attention mechanism and improves the performance of the model. (4) By comparing with the SVM model, it is found that the comparison experiment is not enough to use only word2vec word vectors as input. Since traditional machine learning models greatly rely on feature engineering construction methods, which require a large amount of in-domain knowledge and lexicon resources, rich feature selection strategies, and a lot of model parameter tuning work, SVM models using only word vectors as features do not yield good results. It can also be seen that the deep learning approach reduces the work required in the feature engineering class of methods, and the model itself already has excellent feature extraction capabilities. The feature engineering-based approaches proposed by the researchers have also achieved good results in existing stance representation tasks. In order to discuss the improvement of the pooling layer of convolutional neural networks by the attention mechanism proposed in this paper, the comparison of classification accuracy of different ensemble strategies is shown in Table 4. The method in this paper is compared with a hybrid network model using traditional pooling strategy, and the specific experiments are as follows: (1) BiLSTM-CNN-avg: word vectors trained with word2vec are used, features are extracted using CNN based on average pooling strategy, and text representations learned by BiLSTM are fused for classification. (2) BiLSTM-CNN-1max: word2vec trained word vector, extracted features using CNN based on 1-maxpooling strategy, and fused with text representation learned by BiLSTM for classification. (3) BiLSTM-CNNN-kmax: word2vec trained word vector, extracted features using CNN based on k-maxpooling strategy, and fused with text representation learned by BiLSTM for classification. (4) BiLSTM-CNNN-kmax: word2vec trained word vector, extracted features using CNN based on k-maxpooling strategy, fused with text representation learned by BiLSTM for classification, and fused the text representation learned by BiLSTM for classification.Table 3
Comparison of classification accuracy of different methods.
MethodsFFAVORFAGAINSTFavgSVM0.26330.32510.2942CNN0.31070.44370.3772BILSTM0.40030.58620.4933BILSTM-CNN4 max0.63170.49890.5653Nanyu-NN0.67290.45840.5665BiLSTM-CNN-ATT0.66300.49520.5791Table 4
Comparison of classification accuracy of different pooling strategies.
MethodsFFAVORFAGAINSTFavgBILSTM-CNN-avg0.61310.46170.5374BILSTM-CNN4 max0.63170.49890.5653BILSTM-CNN-kmax0.64790.47830.5631BiLSTM-CNN-ATT0.66300.49520.5791From the comparison experiments with hybrid networks with different pooling strategies, the 1-maxpooling pooling strategy has a slight improvement overk-maxpooling among the traditional pooling strategies, but both are significantly better than the average pooling strategy. The attention-based pooling strategy proposed in this paper has a significant effect improvement relative to the best-performing 1-maxpooling strategy. Compared with the traditional pooling strategy, the improved pooling operation based on the attention mechanism can significantly improve the experimental results and reduce the information loss in the pooling layer. Meanwhile, the attention mechanism achieves the purpose of optimizing the model by highlighting the effective stance features and improves the accuracy of the stance expression task.


## 4.1. Dataset

In this paper, the open corpus dataset provided by the Chinese microblogging stance expression task published by NLPCC is chosen to validate the method of this paper. The dataset provides 3000 training data with labeled stance categories and 1000 test data with known stance category labels. Each data contains a Chinese microblog body, description subject, and stance label. The agreed stance category labels in the dataset are FAVOR, AGAINST, and NONE, which indicate three stances of support, opposition, and no stance, respectively. The dataset is divided into five description subjects, which are #Chinese New Year firecrackers, #IphoneSe, #Russia’s anti-terrorist operation in Syria, #Opening the second child, and #Shenzhen’s motorbike ban.


## 4.2. Data Preprocessing

Chinese microblog texts often contain informal texts such as emojis, URLs, usernames, and the topic hashtag #Hashtag, which is unique to the Chinese microblog corpus. These informal texts pose a great challenge to the stance representation task. In order to remove unnecessary noise interference, this paper preprocesses the Chinese microblog text as follows: (1) Filter all punctuation and special characters and keep only the Chinese and English texts with semantic value information. (2) Format conversion. The full-corner English characters in the original data are converted to half-corner English characters. All English characters are converted to lowercase characters. (3) Remove short links and @ tags in Chinese microblogs. (4) Word separation processing. There is no direct distinction between words in Chinese, so the Chinese text needs to be divided into words. The effect of word separation will directly affect the experimental results. In this paper, we use jieba word separation for Chinese word separation.


## 4.3. Evaluation Indexes

The main evaluation metrics for evaluating classifiers are accuracy and recall. For a given test dataset, the accuracy rate is the ratio of the number of samples correctly classified by the classifier to the total number of samples, and the recall rate is the proportion of correctly determined positive cases to the total number of positive cases, and the two are mutually constrained. To balance the relationship between accuracy and recall, a comprehensive measureF-measure (F-measure) is introduced as a classifier evaluation metric. In the official evaluation criteria provided by NLPCC, the F-measure (FFAVOR) for the support (FAVOR) label and the F-measure (FAGAINST) for the opposition (AGAINST) label need to be calculated, and the macroaverage of the two (Favg) is used as the final evaluation metric. The loss convergence diagram of the training process is shown in Figure 6.Figure 6
Schematic diagram of the loss convergence of the training process.


## 4.4. Experimental Results

TheFavg values for different dropout ratios p and different numbers of filters T are given in Table 2. From the experimental results, when the dropout value p is greater than 0.5, the classification effect of the stance expression decreases significantly. When the number of filters T increases, Favg of stance expression improves, and it is best for stance expression when T reaches 150. When computing similarity, in order to map BiLSTM text representation and local convolutional features to the same dimension, we make T=2H, so the number of convolutional kernels T affects the complexity of the BiLSTM module. In summary, in this paper, under the premise of no significant increase in model training complexity and training time, the final number of 150 filters is chosen, and the dropout ratio p is 0.5 Under this condition, the accuracy of the stance representation method designed in this paper can reach 0.5791.Table 2
The effect of stance expression with a different number of filters and dropout ratio.
T=50T=80T=100T=150p=0.10.49710.50200.53370.5639p=0.30.49330.49730.53720.5701p=0.50.48900.49110.53900.5791p=0.70.46420.47030.49150.4972p=0.90.31970.33210.34800.3540In order to verify the effectiveness of the attention-based hybrid neural network proposed in this paper in the Chinese microblog stance expression method, the method in this paper is compared with traditional machine learning methods (SVM), CNN, BiLSTM, and BiLSTM-CNN hybrid network without added attention mechanism and baseline. The specific experiments are as follows: (1) Traditional machine learning: word2vec model is used to train word vectors as input data, and SVM is used for classification. (2) CNN: word2vec is used to train word vectors, and CNN is trained to extract features and classify. (3) BiLSTM: word2vec is used to train word vectors, and improved BiLSTM is trained to extract features and classify. (4) BiLSTM-CNN-max: word2vec trained word vectors are used, features are extracted using CNN based on 1-maxpooling strategy, and text representation learned by fusion BiLSTM is classified. (5) Nanyu-NN: hybrid neural network method proposed by Nanyu is used as a baseline. (6) In this paper, method BiLSTM-CNN-ATT: the attention-based BiLSTM-CNN hybrid neural network proposed in this paper is used for feature extraction and classification.A comparison of the performance of different methods is shown in Table3. (1) Comparison with baseline Nanyu-NN: the idea of Nanyu is to add LSTM between the convolutional and pooling layers of CNN to learn the text representation again and build a hybrid network model. By comparing with this paper’s method, we can see that this paper uses word2vec as the base word representation to build the BiLSTM-CNN hybrid network model and improves the pooling layer to optimize the features by the attention mechanism. The effect of this paper is 2.23% better than that of the hybrid network model proposed by Nanyu, which can effectively improve the performance of stance representation. (2) By comparing the single-structured CNN and BiLSTM models, it is found that the hybrid network model performs better than the single-structured neural network. Theoretically, as the depth of the model increases, the expressiveness of the model is better due to the increased parameters. However, the models are more complex and more prone to overfitting. In this paper, the balance is performed by dropout strategy and L2 regularity. (3) By comparing the BiLSTM-CNN model with the common pooling strategy without adding the attention mechanism, it is found that the attention mechanism can effectively optimize the features and bring out the important features. The pooling layer mitigates the information loss problem through the attention mechanism and improves the performance of the model. (4) By comparing with the SVM model, it is found that the comparison experiment is not enough to use only word2vec word vectors as input. Since traditional machine learning models greatly rely on feature engineering construction methods, which require a large amount of in-domain knowledge and lexicon resources, rich feature selection strategies, and a lot of model parameter tuning work, SVM models using only word vectors as features do not yield good results. It can also be seen that the deep learning approach reduces the work required in the feature engineering class of methods, and the model itself already has excellent feature extraction capabilities. The feature engineering-based approaches proposed by the researchers have also achieved good results in existing stance representation tasks. In order to discuss the improvement of the pooling layer of convolutional neural networks by the attention mechanism proposed in this paper, the comparison of classification accuracy of different ensemble strategies is shown in Table 4. The method in this paper is compared with a hybrid network model using traditional pooling strategy, and the specific experiments are as follows: (1) BiLSTM-CNN-avg: word vectors trained with word2vec are used, features are extracted using CNN based on average pooling strategy, and text representations learned by BiLSTM are fused for classification. (2) BiLSTM-CNN-1max: word2vec trained word vector, extracted features using CNN based on 1-maxpooling strategy, and fused with text representation learned by BiLSTM for classification. (3) BiLSTM-CNNN-kmax: word2vec trained word vector, extracted features using CNN based on k-maxpooling strategy, and fused with text representation learned by BiLSTM for classification. (4) BiLSTM-CNNN-kmax: word2vec trained word vector, extracted features using CNN based on k-maxpooling strategy, fused with text representation learned by BiLSTM for classification, and fused the text representation learned by BiLSTM for classification.Table 3
Comparison of classification accuracy of different methods.
MethodsFFAVORFAGAINSTFavgSVM0.26330.32510.2942CNN0.31070.44370.3772BILSTM0.40030.58620.4933BILSTM-CNN4 max0.63170.49890.5653Nanyu-NN0.67290.45840.5665BiLSTM-CNN-ATT0.66300.49520.5791Table 4
Comparison of classification accuracy of different pooling strategies.
MethodsFFAVORFAGAINSTFavgBILSTM-CNN-avg0.61310.46170.5374BILSTM-CNN4 max0.63170.49890.5653BILSTM-CNN-kmax0.64790.47830.5631BiLSTM-CNN-ATT0.66300.49520.5791From the comparison experiments with hybrid networks with different pooling strategies, the 1-maxpooling pooling strategy has a slight improvement overk-maxpooling among the traditional pooling strategies, but both are significantly better than the average pooling strategy. The attention-based pooling strategy proposed in this paper has a significant effect improvement relative to the best-performing 1-maxpooling strategy. Compared with the traditional pooling strategy, the improved pooling operation based on the attention mechanism can significantly improve the experimental results and reduce the information loss in the pooling layer. Meanwhile, the attention mechanism achieves the purpose of optimizing the model by highlighting the effective stance features and improves the accuracy of the stance expression task.


## 5. Conclusion

Chinese is a VO-type language, and the presence of many OV sequences is because OV sequences are an important grammatical device in Chinese. OV sequences have denotational utility and act at the lexical and syntactic levels of Chinese. Lexically, OV inflection is a way of constructing nouns in Chinese, and many OV independent structures have a tendency to nominalize, and many OV structures can be used as determiners. Syntactically, pure OV inflections are rejected, and only in certain syntactic contexts can OV inflections be established. Different OV morphologies have different levels of referentiality. Most OV sequences are in the middle of the “denotative-statement” continuum, and the denotation can be transformed from statement to denotation when denotation is enhanced to a certain degree.In recent years, identifying the position tendency of text content has become one of the more important topics in natural language processing. Unlike traditional machine learning, which requires the construction of complex feature sets reflecting the characteristics of the task, and other works in the field of stance representation, which treats different words equally, this paper proposes an attention-based BiLSTM-CNN hybrid network stance representation method based on the existing work. The next step of the work will be to address some complex issues. The next work will be carried out for some short texts with complex grammatical structures to further improve the accuracy of stance representation in Chinese microblogs.



---

*Source: 8903529-2022-09-16.xml*