计算机工程与应用2019，Vol.55Issue(7)：23-29,7.DOI:10.3778/j.issn.1002-8331.1811-0210
基于主动MCNN-SCRF的新能源汽车命名实体识别
Named Entity Recognition for New Energy Vehicles Based on Active MCNN-SCRF
摘要
Abstract
New energy vehicles Named Entity Recognition(NER)is challenged by implicit words boundary, rich unregis-tered 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.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Key words
new energy vehicle NER/deep learning/Semi-Markov CRF(SCRF)/segment feature/active learning分类
信息技术与安全科学引用本文复制引用
马建红,张炳斐,张少光,刘双耀..基于主动MCNN-SCRF的新能源汽车命名实体识别[J].计算机工程与应用,2019,55(7):23-29,7.基金项目
国家自然科学基金(No.61702280) （No.61702280）
江苏省自然科学基金(No.BK20170900) （No.BK20170900）
江苏省高等学校自然科学基金(No.17KJB520025). （No.17KJB520025）
