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.
Springer International Publishing AG
A. Hanbury (Allan) , G. Kazai , A. Rauber (Andreas) , N. Fuhr (Norbert)
COMMIT: Infinity (P01)
European Conference on Information Retrieval
Human-Centered Data Analytics

Gebremeskel, G., & de Vries, A. (2015). Entity-Centric Stream Filtering and Ranking: Filtering and Unfilterable Documents. In A. Hanbury, G. Kazai, A. Rauber, & N. Fuhr (Eds.), Advances in Information Retrieval 37th European Conference on IR Research, ECIR 2015, Vienna, Austria, March 29 - April 2, 2015. Proceedings. Springer International Publishing AG.