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A Multi-strategy Learning Approach to Competitor Identification

  • Tong RuanEmail author
  • Yeli Lin
  • Haofen Wang
  • Jeff Z. Pan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8943)

Abstract

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.

Keywords

Competitor mining Unsupervised learning Distant supervision Wrapper induction 

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References

  1. 1.
    Bao, S., Li, R., Yu, Y., Cao, Y.: Competitor mining with the web. IEEE Transactions on Knowledge and Data Engineering 20(10), 1297–1310 (2008)CrossRefGoogle Scholar
  2. 2.
    Lappas, T., Valkanas, G., Gunopulos, D.: Efficient and domain-invariant competitor mining. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 408–416. ACM (2012)Google Scholar
  3. 3.
    Ciravegna, F., Chapman, S., Dingli, A., Wilks, Y.: Learning to harvest information for the semantic web. In: Bussler, C.J., Davies, J., Fensel, D., Studer, R. (eds.) ESWS 2004. LNCS, vol. 3053, pp. 312–326. Springer, Heidelberg (2004) CrossRefGoogle Scholar
  4. 4.
    Milne, D., Witten, I.H.: Learning to link with wikipedia. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, pp. 509–518. ACM (2008)Google Scholar
  5. 5.
    Limaye, G., Sarawagi, S., Chakrabarti, S.: Annotating and searching web tables using entities, types and relationships. Proceedings of the VLDB Endowment 3(1–2), 1338–1347 (2010)CrossRefGoogle Scholar
  6. 6.
    Agichtein, E., Gravano, L.: Snowball: extracting relations from large plain-text collections. In: Proceedings of the Fifth ACM Conference on Digital Libraries, pp. 85–94. ACM (2000)Google Scholar
  7. 7.
    Banko, M., Cafarella, M.J., Soderland, S., Broadhead, M., Etzioni, O.: Open information extraction for the web. IJCAI 7, 2670–2676 (2007)Google Scholar
  8. 8.
    Etzioni, O., Cafarella, M., Downey, D., Kok, S., Popescu, A.M., Shaked, T., Soderland, S., Weld, D.S., Yates, A.: Web-scale information extraction in knowitall: (preliminary results). In: Proceedings of the 13th International Conference on World Wide Web, pp. 100–110. ACM (2004)Google Scholar
  9. 9.
    Ciravegna, F., Gentile, A.L., Zhang, Z.: Lodie: Linked open data for web-scale information extraction. SWAIE 925, 11–22 (2012)Google Scholar
  10. 10.
    Hao, Q., Cai, R., Pang, Y., Zhang, L.: From one tree to a forest: a unified solution for structured web data extraction. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 775–784. ACM (2011)Google Scholar
  11. 11.
    Gulhane, P., Madaan, A., Mehta, R., Ramamirtham, J., Rastogi, R., Satpal, S., Sengamedu, S.H., Tengli, A., Tiwari, C.: Web-scale information extraction with vertex. In: IEEE 27th International Conference on Data Engineering (ICDE 2011), pp. 1209–1220. IEEE (2011)Google Scholar
  12. 12.
    He, J., Gu, Y., Liu, H., Yan, J., Chen, H.: Scalable and noise tolerant web knowledge extraction for search task simplification. Decision Support Systems 56, 156–167 (2013)CrossRefGoogle Scholar
  13. 13.
    Dalvi, N., Kumar, R., Soliman, M.: Automatic wrappers for large scale web extraction. Proceedings of the VLDB Endowment 4(4), 219–230 (2011)CrossRefGoogle Scholar
  14. 14.
    Gentile, A.L., Zhang, Z., Ciravegna, F.: Web scale information extraction with lodie. In: 2013 AAAI Fall Symposium Series (2013)Google Scholar
  15. 15.
    Mintz, M., Bills, S., Snow, R., Jurafsky, D.: Distant supervision for relation extraction without labeled data. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, vol. 2, pp. 1003–1011. Association for Computational Linguistics (2009)Google Scholar
  16. 16.
    Roth, B., Barth, T., Wiegand, M., Singh, M., Klakow, D.: Effective slot filling based on shallow distant supervision methods. arXiv preprint arXiv:1401.1158 (2014)
  17. 17.
    Roth, B., Barth, T., Chrupała, G., Gropp, M., Klakow, D.: Relationfactory: a fast, modular and effective system for knowledge base population. In: EACL 2014, p. 89 (2014)Google Scholar
  18. 18.
    Xue, C., Wang, H., Jin, B., Wang, M., Gao, D.: Effective chinese organization name linking to a list-like knowledge base. In: Zhao, D., Du, J., Wang, H., Wang, P., Ji, D., Pan, J.Z. (eds.) CSWS 2014. CCIS, vol. 480, pp. 97–110. Springer, Heidelberg (2014) CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Tong Ruan
    • 1
    Email author
  • Yeli Lin
    • 1
  • Haofen Wang
    • 1
  • Jeff Z. Pan
    • 2
  1. 1.East China University of Science and TechnologyShanghaiChina
  2. 2.University of AberdeenAberdeenScotland

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