陈瑛,陈昂轩,董玉博,赵筱钰,侯文俊.基于LSTM的食品安全自动问答系统方法研究[J].农业机械学报,2019,50(Supp):380-384.
CHEN Ying,CHEN Angxuan,DONG Yubo,ZHAO Xiaoyu,HOU Wenjun.Methods of Food Safety Question Answering System Based on LSTM[J].Transactions of the Chinese Society for Agricultural Machinery,2019,50(Supp):380-384.
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基于LSTM的食品安全自动问答系统方法研究   [下载全文]
Methods of Food Safety Question Answering System Based on LSTM   [Download Pdf][in English]
投稿时间:2019-04-20  
DOI:10.6041/j.issn.1000-1298.2019.S0.058
中文关键词:  食品安全  问答系统  答案抽取模型  LSTM技术  深度学习
基金项目:国家自然科学基金项目(61503386)
作者单位
陈瑛 中国农业大学 
陈昂轩 中国农业大学 
董玉博 中国农业大学 
赵筱钰 中国农业大学 
侯文俊 中国农业大学 
中文摘要:为高效、准确、全面获取食品安全相关信息,以食品安全文本为研究对象,采用Lucene全文检索架构和长短期记忆神经网络(Long short term memory, LSTM)构建了食品安全自动问答系统。依托于从互联网爬取的文本作为非结构化数据集,利用检索架构扩充人工标注的问题答案对规模,并以此训练了可以判断问题和答案候选句匹配程度的LSTM模型。基于Lucene检索机制进行答案候选集提取和基于LSTM模型进行答案提取,构建了一个可根据食品安全相关问题给出答案所在句子的问答系统,并对比了基于Lucene直接检索的答案抽取和基于LSTM的答案抽取这两种方法。结果表明,当候选文档数量增加时,基于 LSTM 模型的问题答案匹配方法,其平均准确度始终高于基于Lucene检索方法的平均准确度;而候选句子数量较小时,基于 LSTM 模型的问题答案匹配方法的平均准确度也高于基于Lucene检索方法的平均准确度。
CHEN Ying  CHEN Angxuan  DONG Yubo  ZHAO Xiaoyu  HOU Wenjun
China Agricultural University,China Agricultural University,China Agricultural University,China Agricultural University and China Agricultural University
Key Words:food safety  question answering system  answer retrieval model  LSTM  deep learning
Abstract:Nowadays, food safety issues have been concerned by both governments and consumers. However, the increasing number of food safety related articles makes it difficult to retrieve useful information from the articles in a short time with high accuracy. In order to improve the efficiency and accuracy of accessing food safety information, a question answering system was proposed, which was based on long short term memory (LSTM) and information retrieval techniques. The system relied on the food safety unstructured texts obtained by Web crawler technologies, and question answer pairs were selected by using Lucene, and LSTM was used to predict answers according to matching degrees between question and candidate sentences. Based on Lucene’s retrieval mechanism and the LSTM model, a question answering system was built which can select sentences that were most likely to contain the answer to given questions. The results showed that the proposed system outperformed the baseline which was only based on retrieval mechanism. Moreover, the performance analysis were made for the two methods with respect to the numbers of candidate articles and candidate sentences.

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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