Deep Web Data Source Classification Based on Text Feature Extension and Extraction

Abstract

With the growth of volume of high quality information in the Deep Web, as the key to utilize this information, Deep Web data source classification becomes one topic with great research value. In this paper, we propose a Deep Web data source classification method based on text feature extension and extraction. Firstly, because the data source contains less text, some data sources even contain less than 10 words. In order to classify the data source based on the text content, the original text must be extended. In text feature extension stage, we use the N-gram model to select extension words. Secondly, we proposed a feature extraction and classification method based on Attention-based Bi-LSTM. By combining LSTM and Attention mechanism, we can obtain contextual semantic representation and focus on words that are closer to the theme of the text, so that more accurate text vector representation can be obtained. In order to evaluate the performance of our classification model, some experiments are executed on the UIUC TEL-8 dataset. The experimental result shows that Deep Web data source classification method based on text feature extension and extraction has certain promotion in performance than some existing methods.