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Entity-Centric Stream Filtering and Ranking: Filtering and Unfilterable Documents

  • Gebrekirstos G. Gebremeskel
  • Arjen P. de Vries
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9022)

Abstract

Cumulative Citation Recommendation (CCR) is defined as: given a stream of documents on one hand and Knowledge Base (KB) entities on the other, filter, rank and recommend citation-worthy documents. The pipeline encountered in systems that approach this problem involves four stages: filtering, classification, ranking (or scoring), and evaluation. Filtering is only an initial step that reduces the web-scale corpus into a working set of documents more manageable for the subsequent stages. Nevertheless, this step has a large impact on the recall that can be attained maximally. This study analyzes in-depth the main factors that affect recall in the filtering stage. We investigate the impact of choices for corpus cleansing, entity profile construction, entity type, document type, and relevance grade. Because failing on recall in this first step of the pipeline cannot be repaired later on, we identify and characterize the citation-worthy documents that do not pass the filtering stage by examining their contents.

Keywords

Relevant Document Entity Type News Item Outgoing Link Document Category 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Balog, K., Ramampiaro, H.: Cumulative Citation Recommendation: Classification vs. Ranking. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 941–944 (2013)Google Scholar
  2. 2.
    Balog, K., Ramampiaro, H., Takhirov, N., Nørvåg, K.: Multi-step Classification Approaches to Cumulative Citation Recommendation. In: Proceedings of the 10th Conference on Open Research Areas in Information Retrieval, pp. 121–128 (2013)Google Scholar
  3. 3.
    Baruah, G., Roegiest, A., Smucker, M.D.: The Effect of Expanding Relevance Judgements with Duplicates. In: SIGIR 2014 Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 1159–1162 (2014)Google Scholar
  4. 4.
    Bouvier, V., Bellot, P.: Filtering Entity Centric Documents Using Numerics and Temporals Features within RF Classifier. In: TREC 2013 (2013)Google Scholar
  5. 5.
    Dalton, J., Dietz, L.: A Neighborhood Relevance Model for Entity Linking. In: Proceedings of the 10th Conference on Open Research Areas in Information Retrieval, pp. 149–156 (2013)Google Scholar
  6. 6.
    Dietz, L., Dalton, J.: Umass at TREC 2013 Knowledge Base Acceleration Track. In: TREC 2013 (2013)Google Scholar
  7. 7.
    Dredze, M., McNamee, P., Rao, D., Gerber, A., Finin, T.: Entity disambiguation for knowledge base population. In: Proceedings of the 23rd International Conference on Computational Linguistics, pp. 277–285 (2010)Google Scholar
  8. 8.
    Efron, M., Willis, C., Organisciak, P., Balsamo, B., Lucic, A.: The University of Illinois’ Graduate School of LIS at TREC 2013. In: TREC 2013 (2013)Google Scholar
  9. 9.
    Frank, J.R., Bauer, J., Kleiman-Weiner, M., Roberts, D.A., Tripuraneni, N., Zhang, C., Ré, C., Voohees, E., Soboroff, I.: Evaluating Stream Filtering for Entity Profile Updates for TREC 2013. In: TREC 2013 (2013)Google Scholar
  10. 10.
    Gebremeskel, G.G., He, J., De Vries, A.P., Lin, J.: Cumulative Citation Recommendation: A Feature-aware Comparisons of Approaches. In: Database and Expert Systems Applications (DEXA), pp. 193–197. IEEE (2014)Google Scholar
  11. 11.
    Ji, H., Grishman, R.: Knowledge Base Bopulation: Successful Approaches and Challenges. In: Proceedings of the 49th Annual Meeting of ACL: Human Language Technologies, pp. 1148–1158 (2011)Google Scholar
  12. 12.
    Liu, X., Fang, H.: A Related Entity Based Approach for Knowledge Base Acceleration. In: TREC 2013 (2013)Google Scholar
  13. 13.
    Nia, M.S., Grant, C., Peng, Y., Wang, D.Z., Petrovic, M.: University of Florida Knowledge Base Acceleration. In: TREC 2013 (2013)Google Scholar
  14. 14.
    Robertson, S.E., Soboroff, I.: The TREC 2002 Filtering Track Report. In: TREC 2012 (2002)Google Scholar
  15. 15.
    Wang, J., Song, D., Lin, C.Y., Liao, L.: BIT and MSRA at TREC KBA Track 2013. In: TREC 2013 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Gebrekirstos G. Gebremeskel
    • 1
  • Arjen P. de Vries
    • 1
  1. 1.Information Access, CWI, AmsterdamAmsterdamThe Netherlands

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