An Effective Algorithm for Classification of Text with Weak Sequential Relationships

Text classification is a fundamental task that is widely used in various sub-domains of natural language processing, such as information extraction, semantic understanding, etc. For the general text classification problems, various deep learning models, such as Bi-LSTM, Transformer, BERT, etc. have been used which achieved good performance. In this paper, however, we consider a new problem on how to deal with a special scenario in text classification which has a weak sequential relationship among different classification entities. A typical example is in the block classification of resumes where there are sequential relationships existing amongst different blocks. By fully utilizing this useful sequential feature, we in this paper propose an effective hybrid model which combines a fully connected neural network model and a block-level recurrent neural network model with feature fusion that makes full use of such a sequential feature. The experimental results show that the average F1-score value of our model on three 1,400 real resume datasets is 5.5–11% higher than the existing mainstream algorithms.