performance. The main contribution of the

paper are an in-depth analysis of the factors that affect entity-based

stream filtering, identifying optimal entity profiles without

compromising precision, describing and classifying relevant documents

that are not amenable to filtering , and estimating the upper-bound

of recall on entity-based filtering.

The rest of the paper  is organized as follows. Section \ref{sec:desc}

describes the dataset and section \ref{sec:fil} defines the task. In

section  \ref{sec:lit}, we discuss related literature folowed by a

discussion of our method in \ref{sec:mthd}. Following that,  we

analyze them in \ref{sec:analysis}. Towards the end, we discuss the

impact of filtering choices on classification in section

\ref{sec:impact}, examine and categorize unfilterable documents in

section \ref{sec:unfil}. Finally, we present our conclusions in

\ref{sec:conc}.

 \section{Data Description}\label{sec:desc}

We base this analysis on the TREC-KBA 2013 dataset%

\footnote{\url{http://trec-kba.org/trec-kba-2013.shtml}}

that consists of three main parts: a time-stamped stream corpus, a set of