Understanding people's emotions through natural language is a challenging task for intelligent systems based on Internet of Things(Io T). The major difficulty is caused by the lack of basic knowledge in emotion ex...Understanding people's emotions through natural language is a challenging task for intelligent systems based on Internet of Things(Io T). The major difficulty is caused by the lack of basic knowledge in emotion expressions with respect to a variety of real world contexts. In this paper, we propose a Bayesian inference method to explore the latent semantic dimensions as contextual information in natural language and to learn the knowledge of emotion expressions based on these semantic dimensions. Our method synchronously infers the latent semantic dimensions as topics in words and predicts the emotion labels in both word-level and document-level texts. The Bayesian inference results enable us to visualize the connection between words and emotions with respect to different semantic dimensions. And by further incorporating a corpus-level hierarchy in the document emotion distribution assumption, we could balance the document emotion recognition results and achieve even better word and document emotion predictions. Our experiment of the wordlevel and the document-level emotion predictions, based on a well-developed Chinese emotion corpus Ren-CECps, renders both higher accuracy and better robustness in the word-level and the document-level emotion predictions compared to the state-of-theart emotion prediction algorithms.展开更多
方案提出一种基于SpanBERT(Bidirectional Encoder Representations from Transformers by representing and predicting Spans)模型的服务热线文本情感分析方法,以SpanBERT实现句向量优化的文本情感细粒度分析方案,针对移动客服与用户...方案提出一种基于SpanBERT(Bidirectional Encoder Representations from Transformers by representing and predicting Spans)模型的服务热线文本情感分析方法,以SpanBERT实现句向量优化的文本情感细粒度分析方案,针对移动客服与用户对话数据,实现场景化客服文本分析,通过挖掘负面投诉对话文本价值,并基于识别的客户情绪、语义信息等进行质检,可提前获知客户的潜在不满意倾向,持续提高客户的服务体验,具有很好的推广前景。已应用在天津移动满意度预测、服务运营分析和语音质检工作中,以投诉语音质检机器人替代人工操作,实现降本增效。展开更多
Understanding is the essence of any intelligent system.We revise our four machine understanding paradigms which are:(i)basic understanding,(ii)rich understanding,(iii)exploratory understanding,and(iv)theory-based unde...Understanding is the essence of any intelligent system.We revise our four machine understanding paradigms which are:(i)basic understanding,(ii)rich understanding,(iii)exploratory understanding,and(iv)theory-based understanding;and we elaborate on the first two of them.We then introduce the concept of two-stage(or deep)machine understanding which provides descriptive understandings,as well as evaluations of these understandings.After a brief systematization of emotions,we cover the following paradigms for agents with two-stage(deep)understanding abilities for emotional intelligence simulation:(i)basic understanding,(ii)rich-understanding,and(iii)switchable understanding.展开更多
基金supported in part by the National Natural Science Foundation of China(NSFC)Key Program(61573094)Fundamental Research Funds for the Central Universities(N140402001)
文摘Understanding people's emotions through natural language is a challenging task for intelligent systems based on Internet of Things(Io T). The major difficulty is caused by the lack of basic knowledge in emotion expressions with respect to a variety of real world contexts. In this paper, we propose a Bayesian inference method to explore the latent semantic dimensions as contextual information in natural language and to learn the knowledge of emotion expressions based on these semantic dimensions. Our method synchronously infers the latent semantic dimensions as topics in words and predicts the emotion labels in both word-level and document-level texts. The Bayesian inference results enable us to visualize the connection between words and emotions with respect to different semantic dimensions. And by further incorporating a corpus-level hierarchy in the document emotion distribution assumption, we could balance the document emotion recognition results and achieve even better word and document emotion predictions. Our experiment of the wordlevel and the document-level emotion predictions, based on a well-developed Chinese emotion corpus Ren-CECps, renders both higher accuracy and better robustness in the word-level and the document-level emotion predictions compared to the state-of-theart emotion prediction algorithms.
文摘方案提出一种基于SpanBERT(Bidirectional Encoder Representations from Transformers by representing and predicting Spans)模型的服务热线文本情感分析方法,以SpanBERT实现句向量优化的文本情感细粒度分析方案,针对移动客服与用户对话数据,实现场景化客服文本分析,通过挖掘负面投诉对话文本价值,并基于识别的客户情绪、语义信息等进行质检,可提前获知客户的潜在不满意倾向,持续提高客户的服务体验,具有很好的推广前景。已应用在天津移动满意度预测、服务运营分析和语音质检工作中,以投诉语音质检机器人替代人工操作,实现降本增效。
文摘Understanding is the essence of any intelligent system.We revise our four machine understanding paradigms which are:(i)basic understanding,(ii)rich understanding,(iii)exploratory understanding,and(iv)theory-based understanding;and we elaborate on the first two of them.We then introduce the concept of two-stage(or deep)machine understanding which provides descriptive understandings,as well as evaluations of these understandings.After a brief systematization of emotions,we cover the following paradigms for agents with two-stage(deep)understanding abilities for emotional intelligence simulation:(i)basic understanding,(ii)rich-understanding,and(iii)switchable understanding.
