Conversational Analysis of Power and Solidarity in Chinese Community Correction Discourse

This paper mainly discusses the power and solidarity among participants in the community correction discourse from sociolinguistic perspective by adopting the methodology of conversation analysis.Through the comparative study of the steps of Initial Evaluation and Entry Ceremony in community correction,this paper analyses the power and solidarity in the features of adjacency pair and turn-taking,and finds that the step of Initial Evaluation is relatively negotiable and represents the social relationship of solidarity,while the step of Entry Ceremony stresses more on social distance and power relationship.The reasons that cause the power and solidarity in the Initial Evaluation and the Entry Ceremony from the perspective of social identity and social purposes are also explored.

Prototype and English Vocabulary Teaching and Learning

English vocabulary teaching and learning is indispensable in the process of English teaching and learning.For most of college students,the quantitative of English vocabulary they master is often not enough,which not only influences students for learning English,but also brings about certain obstacles in developing diverse teaching and learning activities.The paper tends to be based on prototype to shed some light on English vocabulary pedagogy can provide to EFL,showing the necessity to apply educational strategies to improve the students’English vocabulary acquisition.

Deep Broad Learning for Emotion Classification in Textual Conversations

Emotion classification in textual conversations focuses on classifying the emotion of each utterance from textual conversations.It is becoming one of the most important tasks for natural language processing in recent years.However,it is a challenging task for machines to conduct emotion classification in textual conversations because emotions rely heavily on textual context.To address the challenge,we propose a method to classify emotion in textual conversations,by integrating the advantages of deep learning and broad learning,namely DBL.It aims to provide a more effective solution to capture local contextual information(i.e.,utterance-level)in an utterance,as well as global contextual information(i.e.,speaker-level)in a conversation,based on Convolutional Neural Network(CNN),Bidirectional Long Short-Term Memory(Bi-LSTM),and broad learning.Extensive experiments have been conducted on three public textual conversation datasets,which show that the context in both utterance-level and speaker-level is consistently beneficial to the performance of emotion classification.In addition,the results show that our proposed method outperforms the baseline methods on most of the testing datasets in weighted-average F1.