ass日本风韵熟妇pics男人扒开女人屁屁桶到爽|扒开胸露出奶头亲吻视频|邻居少妇的诱惑|人人妻在线播放|日日摸夜夜摸狠狠摸婷婷|制服 丝袜 人妻|激情熟妇中文字幕|看黄色欧美特一级|日本av人妻系列|高潮对白av,丰满岳妇乱熟妇之荡,日本丰满熟妇乱又伦,日韩欧美一区二区三区在线

The notable feature of a question answering system is to understand the semantic information of the user’s question. Question classification, as the key module of question answering system, plays a decisive role in the efficiency of system retrieval. In order to classify the user’s questions, a classification model of tomato pests and diseases based on word2vec and bidirectional gated recurrent unit (BIGRU) was constructed. word2vec was used to transform the words in the sentence into the word vector with semantic information. The word vector was used as the initial corpus. Two neural network methods and a machine learning method were adopted to train the classification model. Totally 2000 tomato pests and diseases related questions were selected, which were divided into two categories: tomato diseases and tomato pests. The results showed that the classification accuracy, recall rate and F1 value by using the BIGRU model were 2~5 percentage points higher than those by using convolutional ceural network (CNN) and K-nearest neighbor (KNN) classification algorithm. Further experimental results comparison indicated that the BIGRU model performed the best on tomato pest and diseases question classification. The BIGRU model was simple in structure, less in model training parameters, and fast in training speed. It met the response time requirements of question answering system.