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Alternative TitleResearch on the Algorithm of Evaluation Collocation Extraction Based on Word Vector
杨令铎; 史海波; 周晓锋
Source Publication小型微型计算机系统
Indexed ByCSCD
Contribution Rank1
Funding Organization国家“八六三”高技术研究发展计划项目(2013AA040705-1)资助
Keyword搭配抽取 词向量 神经网络 条件随机域 最大熵
Other AbstractMaximum entropy and conditional random field or other algorithms used for collocation extraction in the traditional assessment of Chinese language rely on manual selection of characteristics and have a high demand for semantics marking precision at the preliminary stage. In this paper,an alternative approach is suggested which substitutes term vector for the traditional semantic characteristics in collocation extracting. Specifically,the term vectors are acquired by an in-depth model completing unsupervised learning from a large corpus. In testing,the term vectors and the semantic characteristics are separately entered as inputs into three machine learning models. The results indicate that better outcomes are produced when term vectors are used in the neural network model in the sense that both the precision and recall rate are higher by nearly 3% than the best outcomes that are achievable with semantic characteristics. We also note that as the size of the corpus used for unsupervised learning training increases the resulting term vectors become more and more pragmatic.
Citation statistics
Document Type期刊论文
Corresponding Author杨令铎
Recommended Citation
GB/T 7714
杨令铎,史海波,周晓锋. 基于词向量的评价搭配抽取算法研究[J]. 小型微型计算机系统,2016,37(10):2269-2272.
APA 杨令铎,史海波,&周晓锋.(2016).基于词向量的评价搭配抽取算法研究.小型微型计算机系统,37(10),2269-2272.
MLA 杨令铎,et al."基于词向量的评价搭配抽取算法研究".小型微型计算机系统 37.10(2016):2269-2272.
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