基于FastText和关键句提取的中文长文本分类

Chinese Long Text Classification Based on FastText and Key Sentence Extraction

FastText是一种准确高效的文本分类模型,但直接应用在中文长文本分类领域存在准确度不高的问题.针对该问题,提出一种融合TextRank关键子句提取和词频-逆文本频率(Term Frequency-Inverse Document Frequency,TF-IDF)的FastText中文长文本分类方法.该方法在FastText模型输入阶段使用TextRank算法提取文本的关键子句输入训练模型,同时采用TF-IDF提取文本的关键词作为特征补充,从而在减少训练语料的同时尽可能保留文本分类的关键特征.实验结果表明,此文本分类方法在数据集上准确率达到86.1%,比经典的FastText模型提高了约4%.

FastText is a precise and efficient text classification model,but the precision is low when it is directly applied to Chinese long text classification.Regarding this problem,this study proposes a FastText method for Chinese long text classification,which combines TextRank key clause extraction with Term Frequency-Inverse Document Frequency(TF-IDF).Firstly,TextRank is used to extract the key clauses of the text as input features.Secondly,key words of the text are extracted by TF-IDF as a feature supplement.Finally,the extracted text features are input into the FastText model,which can preserve the key features of the target text while reducing the training corpus.The experimental results show that the accuracy of the proposed method on the datasets is 86.1%,which is about 4%higher than the classic FastText model.