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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
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)
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)
Bouvier, V., Bellot, P.: Filtering Entity Centric Documents Using Numerics and Temporals Features within RF Classifier. In: TREC 2013 (2013)
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)
Dietz, L., Dalton, J.: Umass at TREC 2013 Knowledge Base Acceleration Track. In: TREC 2013 (2013)
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)
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)
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)
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)
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)
Liu, X., Fang, H.: A Related Entity Based Approach for Knowledge Base Acceleration. In: TREC 2013 (2013)
Nia, M.S., Grant, C., Peng, Y., Wang, D.Z., Petrovic, M.: University of Florida Knowledge Base Acceleration. In: TREC 2013 (2013)
Robertson, S.E., Soboroff, I.: The TREC 2002 Filtering Track Report. In: TREC 2012 (2002)
Wang, J., Song, D., Lin, C.Y., Liao, L.: BIT and MSRA at TREC KBA Track 2013. In: TREC 2013 (2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Gebremeskel, G.G., de Vries, A.P. (2015). Entity-Centric Stream Filtering and Ranking: Filtering and Unfilterable Documents. In: Hanbury, A., Kazai, G., Rauber, A., Fuhr, N. (eds) Advances in Information Retrieval. ECIR 2015. Lecture Notes in Computer Science, vol 9022. Springer, Cham. https://doi.org/10.1007/978-3-319-16354-3_33
Download citation
DOI: https://doi.org/10.1007/978-3-319-16354-3_33
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16353-6
Online ISBN: 978-3-319-16354-3
eBook Packages: Computer ScienceComputer Science (R0)