赵明,杜会芳,董翠翠,陈长松.基于word2vec和LSTM的饮食健康文本分类研究[J].农业机械学报,2017,48(10):202-208.
ZHAO Ming,DU Huifang,DONG Cuicui,CHEN Changsong.Diet Health Text Classification Based on word2vec and LSTM[J].Transactions of the Chinese Society for Agricultural Machinery,2017,48(10):202-208.
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基于word2vec和LSTM的饮食健康文本分类研究   [下载全文]
Diet Health Text Classification Based on word2vec and LSTM   [Download Pdf][in English]
投稿时间:2017-01-13  
DOI:10.6041/j.issn.1000-1298.2017.10.025
中文关键词:  文本分类  word2vec  词向量  长短期记忆网络  K-means++
基金项目:信息网络安全公安部重点实验室开放课题项目(61503386)
作者单位
赵明 中国农业大学 
杜会芳 中国农业大学 
董翠翠 中国农业大学 
陈长松 公安部第三研究所 
中文摘要:为了对饮食文本信息高效分类,建立一种基于word2vec和长短期记忆网络(Long-short term memory,LSTM)的分类模型。针对食物百科和饮食健康文本特点,首先利用word2vec实现包含语义信息的词向量表示,并解决了传统方法导致数据表示稀疏及维度灾难问题,基于K-means++根据语义关系聚类以提高训练数据质量。由word2vec构建文本向量作为LSTM的初始输入,训练LSTM分类模型,自动提取特征,进行饮食宜、忌的文本分类。实验采用48000个文档进行测试,结果显示,分类准确率为98.08%,高于利用tf-idf、bag-of-words等文本数值化表示方法以及基于支持向量机(Support vector machine,SVM)和卷积神经网络(Convolutional neural network,CNN)分类算法结果。实验结果表明,利用该方法能够高质量地对饮食文本自动分类,帮助人们有效地利用健康饮食信息。
ZHAO Ming  DU Huifang  DONG Cuicui  CHEN Changsong
China Agricultural University,China Agricultural University,China Agricultural University and The Third Research Institute, Ministry of Public Security
Key Words:text classification  word2vec  word embedding  long-short term memory network  K-means++
Abstract:text classification;word2vec;word embedding;long-short term memory network;K-means++

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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