Column Concept Determination for Chinese Web Tables via Convolutional Neural Network

Abstract

Hundreds of millions of tables on the Internet contain a considerable wealth of high-quality relational data. However, the web tables tend to lack explicit key semantic information. Therefore, information extraction in tables is usually supplemented by recovering the semantics of tables, where column concept determination is an important issue. In this paper, we focus on column concept determination in Chinese web tables. Different from previous research works, convolutional neural network (CNN) was applied in this task. The main contributions of our work lie in three aspects: firstly, datasets were constructed automatically based on the infoboxes in Baidu Encyclopedia; secondly, to determine the column concepts, a CNN classifier was trained to annotate cells in tables and the majority vote method was used on the columns to exclude incorrect annotations; thirdly, to verify the effectiveness, we performed the method on the real tabular dataset. Experimental results show that the proposed method outperforms the baseline methods and achieves an average accuracy of 97% for column concept determination.