Entity-centric information processing requires complex pipelines

involving both natural language processing and information retrieval

components. In entity-centric stream filtering and ranking, the

pipeline involves four stages: filtering, classification,

ranking (scoring) and evaluation. Filtering is an initial step, that

extracts a working-set of documents from the web-scale corpus, aiming

for a smaller size collection that would be more manageable in the

subsequent stages of the pipeline. This filtering step therefore

determines the maximally attainable performance of the overall system.

This paper investigates the filtering stage in isoltation, in context

of a cumulative citation recommendation problem. We conduct an

in-depth analysis of the main factors that determine filtering

effectiveness: cleansing noisy web data, methods to create entity

profiles, the types of entity of interest, document category, and the

relevance level of the entity-document pair under consideration.

We analyze how these factors (and the design choices made in their

corresponding system components) affect filtering performance.

We identify and characterize the relevant documents that do not pass the

filtering stage, and conduct a manual examination into their

contents. The paper classifies the ways unfilterable documents  

are mentioned in text and estimates the practical upper-bound of

recall in entity-based filtering.