Text similarity based on two independent channels: Siamese Convolutional Neural Networks and Siamese Recurrent Neural Networks

In the present-day context, a large amount of information exists in text. It is hard to extract meaningful and potential information from the text. From the current research, text similarity provides a method applied in many practical scenarios. Traditional text similarity algorithms are easy to implement, but more than these algorithms are needed to extract text features. At present, most text similarity algorithms are based on deep learning. However, these algorithms often struggle with adequately extracting both local and context text features, and they typically do not differentiate between the effectiveness of these two types of feature extraction. To address these shortcomings, this paper proposes Two Independent Channels: Siamese Convolutional Neural Networks and Siamese Recurrent Neural Networks (TIC-SCNN-SRNN). This approach is designed for binary classification, where ‘1’ indicates similarity and ‘0’ indicates dissimilarity. In detail, this paper proposes the Siamese Convolutional Neural Networks (SCNN) model to address the issue of insufficient extraction of local text features. Additionally, it introduces the Siamese Recurrent Neural Networks (SRNN) model to tackle the problem of insufficient extraction of context text features. Due to the issue of not distinguishing the effects of local and context text features extractions, this paper conducts independent weight learning on these two models to research which is more effective for text similarity tasks. In order to verify the effectiveness of the model, this paper experiments on SciTail, TwitterPPD, and QQP datasets. The experimental results show that SCNN and SRNN influence the text similarity tasks, but SRNN is more effective than SCNN. Furthermore, to verify the advantages of the TIC-SCNN-SRNN model, this paper tests the performance of the state-of-the-art CNN&RNN models. The test results show that the TIC-SCNN-SRNN model performs the best, indicating that the model proposed in this paper is more effective for the text similarity tasks.