基于卷积模型的农业问答语性特征抽取分析

互联网农技推广社区每秒增衍问答数据近万组，这些海量数据具有隐性的词性、情感和冗余向量特征，实现数据聚合与数据块消减是该领域的难题。提出了一种基于卷积神经网络的农业问答情感极性特征抽取分析模型，结合农业分词字典，对数据集进行分词后使用Skip-gram模型转换为256维的词向量，利用批规范后的卷积神经网络对数据集进行训练，从而得到用于识别农技推广社区问答词性情感相似性的神经网络模型参数。试验结果表明，该方法能够准确识别测试样例集中的冗余队列，与其他5种文本分类方法进行比较，各项指标优势明显，针对测试集的语性特征抽取准确率达到82.7%。

Analysis of Extraction of Semantic Feature in Agricultural Question and Answer Based on Convolutional Model

Tens of thousands of question and answer data have been increased per second in the internet agricultural technology extension community, these massive data have features of recessive part of speech, emotion and unwanted vectors, and how to implement data aggregation and data block reduction is the difficult problem in this field. An analytical model for the extraction of emotional polarity in agricultural question and answer based on convolutional neural network was proposed, the training set was transformed into a 256-dimensional word vector by using the Skip-gram model after segmenting the dataset with agricultural word segmentation dictionary. The convolution neural network after batch-normalization specification was used to train the dataset, and the neural network model parameters used to identify the part of speech emotional similarities in the agricultural technology promotion community question and answer were obtained. The experimental results showed that the method could accurately identify redundant queues in the test sample set, and by comparing with the other four text classification methods, there were also obvious advantages in each index, the accuracy of the semantic feature extraction for the test set was up to 82.7%.