Information Filtering: Overview of Issues, Research and Systems
User Modeling and User-Adapted Interaction
Topic detection and tracking: event-based information organization
Topic detection and tracking: event-based information organization
Wikify!: linking documents to encyclopedic knowledge
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Open information extraction from the web
Communications of the ACM - Surviving the data deluge
Learning to link with wikipedia
Proceedings of the 17th ACM conference on Information and knowledge management
Knowledge base population: successful approaches and challenges
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Multi-step classification approaches to cumulative citation recommendation
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
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Cumulative citation recommendation refers to the task of filtering a time-ordered corpus for documents that are highly relevant to a predefined set of entities. This task has been introduced at the TREC Knowledge Base Acceleration track in 2012, where two main families of approaches emerged: classification and ranking. In this paper we perform an experimental comparison of these two strategies using supervised learning with a rich feature set. Our main finding is that ranking outperforms classification on all evaluation settings and metrics. Our analysis also reveals that a ranking-based approach has more potential for future improvements.