文献类型：期刊文献

英文题名：A multi-strategy learning approach to competitor identification

作者：Ruan, Tong[1]; Lin, Yeli[1]; Wang, Haofen[1]; Pan, Jeff Z.[2]

机构：[1] East China University of Science and Technology, Shanghai, 200237, China; [2] University of Aberdeen, Aberdeen, United Kingdom

年份：2015

卷号：8943

起止页码：197

外文期刊名：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

收录：EI(收录号：20151900830937)

语种：英文

外文关键词：Iterative methods - Natural language processing systems

摘要：Competitor identification tries to find competitors of some entity in a given field, which is the key to the success of market intelligence. Manually collecting competitors is labor-intensive and time consuming. So automatic approaches are proposed for this purpose. However, these approaches suffer from the following two main challenges. Competitor information might not only be contained in semi-structured sources like lists or tables, but also be mentioned in free texts. The diversity of its sources make competitor identification quite difficult. Also, these competitors might not always occur in form of their full names. The occurrences of name variants further increase the diversity, and make the task more challenging. In this paper, we propose a novel unsupervised approach to identify competitors from prospectuses based on a multi-strategy learning algorithm. More precisely, we first extract competitors from lists using some predefined heuristic rules. By leveraging redundancies among competitor information in lists, tables, and texts, these competitors are fed as seeds to distantly supervise the learning process to find table columns and text patterns containing competitors. The whole process is iteratively performed. In each iteration, the newly discovered competitors of high confidence from various sources are treated as new seeds for bootstrapping. The experimental results show the effectiveness of our approach without human intentions and external knowledge bases. Moreover, the approach significantly outperforms traditional named entity recognition approaches. ? Springer International Publishing Switzerland 2015.