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
A self-adaptive learning based immune algorithm （SALIA） is proposed to tackle diverse optimization problems, such as complex multi-modal and ill-conditioned prc,blems with the high robustness. The SALIA algorithm ad...A self-adaptive learning based immune algorithm （SALIA） is proposed to tackle diverse optimization problems, such as complex multi-modal and ill-conditioned prc,blems with the high robustness. The SALIA algorithm adopted a mutation strategy pool which consists of four effective mutation strategies to generate new antibodies. A self-adaptive learning framework is implemented to select the mutation strategies by learning from their previous performances in generating promising solutions. Twenty-six state-of-the-art optimization problems with different characteristics, such as uni-modality, multi-modality, rotation, ill-condition, mis-scale and noise, are used to verify the validity of SALIA. Experimental results show that the novel algorithm SALIA achieves a higher universality and robustness than clonal selection algorithms （CLONALG）, and the mean error index of each test function in SALIA decreases by a factor of at least 1.0×10^7 in average.展开更多
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。
基金Project(2010ZC13012) supported by the Aviation Science Funds of China
文摘A self-adaptive learning based immune algorithm （SALIA） is proposed to tackle diverse optimization problems, such as complex multi-modal and ill-conditioned prc,blems with the high robustness. The SALIA algorithm adopted a mutation strategy pool which consists of four effective mutation strategies to generate new antibodies. A self-adaptive learning framework is implemented to select the mutation strategies by learning from their previous performances in generating promising solutions. Twenty-six state-of-the-art optimization problems with different characteristics, such as uni-modality, multi-modality, rotation, ill-condition, mis-scale and noise, are used to verify the validity of SALIA. Experimental results show that the novel algorithm SALIA achieves a higher universality and robustness than clonal selection algorithms （CLONALG）, and the mean error index of each test function in SALIA decreases by a factor of at least 1.0×10^7 in average.
