摘要
语义理解是自然语言理解的一项关键任务,传统上采用以语法为中心的词法和句法分析等技术来解析句义。该文提出了一种以语义块分析藏文句义的新方法,其中藏文语义块识别通过采用Bi-LSTM和ID-CNN两种神经网络构架对该任务进行建模和对比分析。经实验,上述的两种模型在测试数据集上取得了良好的性能表现,F1值平均分别为89%和92%。这种语义块分析和识别技术能够较好地替代词义消歧和语义角色标注等工作。
Semantic understanding is an essential task in natural language understanding.Conventionally,grammarrule-based approaches including lexical and sentence analysis are leveraged to parse the semantic meaning of given text.In this work,we present a new method to address Tibetan sentence semantic parsing via semantic chunking.The semantic chunking is modeled by Bi-LSTM and ID-CNN neural network,respectively.In experiments,the proposed model shows a remarkable performance,achieving the average F1 of 89% and 92%,respectively.
作者
柔特
色差甲
才让加
ROU Te;SE Chajia;CAI Rangjia(Department of Computer Science,Qinghai Normal University,Xining,Qinghai 810016,China;Provincial Key Laboratory of Tibetan Intelligent information Processing and Machine Translation,Qinghai Normal University, Xining, Qinghai 810008, China)
出处
《中文信息学报》
CSCD
北大核心
2019年第6期42-49,共8页
Journal of Chinese Information Processing
基金
国家重点研发计划项目(2017YFB1402200)
国家自然科学基金(61662061)
国家社会科学基金(14BYY132,15BYY167,16YY167)
关键词
藏文
语义块
语义分割
语义分析
Tibetan
semantic chunk
semantic segmentation
semantic analysis
