关键词: 新能源汽车命名实体识别, 深度学习, 半马尔可夫条件随机场, 片段特征, 主动学习

Abstract: New energy vehicles Named Entity Recognition（NER） is challenged by implicit words boundary, rich unregistered words and lack of labeled dataset, resulting for low identification precision and recall. This paper presents a NER model based Multiple Channel Neural Network（MCNN）, which incorporates features of characters, words and segments. The model does not regard the NER task as a sequence labelling problem, but uses Semi-Markov CRF（SCRF） to learn the effective segment-level representation and contextual information and then assign tags to the segments. In response to the lack of labeled dataset, this model uses an active query strategy based on uncertainty and density to select unlabeled data for future training. The strategy proposed makes the data with representative information and uncertainty have much higher selection opportunity and improves the system learning ability effectively. Experiments show that this model can improve the precision and recall while reducing manual annotation efforts greatly.

Key words: new energy vehicle NER, deep learning, Semi-Markov CRF（SCRF）, segment feature, active learning