A New Vision-Based Method for Extracting Academic Information from Conference Web Pages

摘要

This paper proposes a new vision-based method for extracting academic information from conference Web pages. The main contributions include: (1) An new vision-based page segmentation algorithm is proposed to improve the result of classical VIPS algorithm. This algorithm can divide pages into text blocks. (2) All text blocks are classified as 10 categories according to vision features, keyword features and text content features. The initial classification results have 75% precision and 67% recall. (3) The context information of text blocks are employed to repair and refine initial classification results, which are improved to 96% precision and 98% recall. Finally, academic information is extracted from classified text blocks. Our experimental results on real-world datasets show that the proposed method is effective and efficient for extracting academic information from conference Web pages.