In such a situation, it becomes hard to identify the factors that

result in improved performance. There is  a lack of insight across

different approaches. This makes  it hard to know whether the

improvement in performance of a particular approach is due to

preprocessing, filtering, classification, scoring  or any of the

sub-components of the pipeline.

In this paper, we therefore fix the subsequent steps of the pipeline,

and zoom in on \emph{only} the filtering step; and conduct an in-depth analysis of its

main components.  In particular, we study the effect of cleansing,

entity profiling, type of entity filtered for (Wikipedia or Twitter), and

document category (social, news, etc) on the filtering components'

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 is organized as follows: 

\textbf{TODO!!}

 \section{Data Description}

KB entities to be curated, and a set of relevance judgments. A CCR

system now has to identify for each KB entity which documents in the

stream corpus are to be considered by the human curator.

\subsection{Stream corpus} The stream corpus comes in two versions:

raw and cleaned. The raw and cleansed versions are 6.45TB and 4.5TB

respectively,  after xz-compression and GPG encryption. The raw data

is a  dump of  raw HTML pages. The cleansed version is the raw data

after its HTML tags are stripped off and only English documents

identified with Chromium Compact Language Detector

\footnote{https://code.google.com/p/chromium-compact-language-detector/}

are included.  The stream corpus is organized in hourly folders each

of which contains many  chunk files. Each chunk file contains between

hundreds and hundreds of thousands of serialized  thrift objects. One

thrift object is one document. A document could be a blog article, a

news article, or a social media post (including tweet).  The stream

corpus comes from three sources: TREC KBA 2012 (social, news and

linking) \footnote{http://trec-kba.org/kba-stream-corpus-2012.shtml},

arxiv\footnote{http://arxiv.org/}, and

spinn3r\footnote{http://spinn3r.com/}.

Table \ref{tab:streams} shows the sources, the number of hourly

directories, and the number of chunk files.

\begin{table}

\caption{Retrieved documents to different sources }

\subsection{KB entities}

 The KB entities consist of 20 Twitter entities and 121 Wikipedia entities. The selected entities are, on purpose, sparse. The entities consist of 71 people, 1 organization, and 24 facilities.  

\subsection{Relevance judgments}

TREC-KBA provided relevance judgments for training and

testing. Relevance judgments are given as a document-entity

pairs. Documents with citation-worthy content to a given entity are

annotated  as \emph{vital},  while documents with tangentially

relevant content, or documents that lack freshliness o  with content

that can be useful for initial KB-dossier are annotated as

\emph{relevant}. Documents with no relevant content are labeled

\emph{neutral} and spam is labeled as \emph{garbage}. 

%The inter-annotator agreement on vital in 2012 was 70\% while in 2013 it

%is 76\%. This is due to the more refined definition of vital and the

%distinction made between vital and relevant.

\subsection{Breakdown of results by document source category}

%The results of the different entity profiles on the raw corpus are

%broken down by source categories and relevance rank% (vital, or

%relevant).  

are very similar to each other. Mainstream-news and news are

similar. The reason they exist separately, in the first place,  is

because they were collected from two different sources, by different

groups and at different times. we call them news from now on.  The

same is true with weblog and social, and we call them social from now

on.   2) some groups have so small number of annotations that treating

them independently does not make much sense. Majority of vital or

relevant annotations are social (social and weblog) (63.13\%). News

(mainstream +news) make up 30\%. Thus, news and social make up about

93\% of all annotations.  The rest make up about 7\% and are all

grouped as others.

 \section{Stream Filtering}

 The TREC Filtering track defines filtering as a ``system that sifts

 through stream of incoming information to find documents that are

 relevant to a set of user needs represented by profiles''

 \cite{robertson2002trec}. Its information needs are long-term and are

 whose adhoc information need is represented by a search

 query. Adaptive Filtering, one task of the filtering track,  starts

 with  a persistent user profile and a very small number of positive

 examples. A filtering system can improve its user profiles with a

 feedback obtained from interaction with users, and thereby improve

 its performance. The  filtering stage of entity-based stream

 filtering and ranking can be likened to the adaptive filtering task

 of the filtering track. The persistent information needs are the KB

 entities, and the relevance judgments are the small number of postive

 examples.

 possible.  Specifically, we conduct in-depth analysis on the choices

 and factors affecting the cleansing step, the entity-profile

 construction, the document category of the stream items, and the type

\begin{enumerate}

  \item Does cleansing affect filtering and subsequent performance

  \item What is the most effective way of entity profile representation

  \item Is filtering different for Wikipedia and Twitter entities?

  \item Are some type of documents easily filterable and others not?

  \item Does a gain in recall at filtering step translate to a gain in F-measure at the end of the pipeline?

\end{enumerate}

The TREC filtering and the filtering as part of the entity-centric

stream filtering and ranking pipepline have different purposes. The

TREC filtering track's goal is the binary classification of documents:

for each incoming docuemnt, it decides whether the incoming document

is relevant or not for a given profile. The docuemnts are either

relevant or not. In our case, the documents have relevance ranking and

the goal of the filtering stage is to filter as many potentially

relevant documents as possible, but less  irrelevant documents as

possible not to obfuscate the later stages of the piepline.  Filtering

as part of the pipeline needs that delicate balance between retrieving

relavant documents and irrrelevant documensts. Bcause of this,

filtering in this case can only be studied by binding it to the later

stages of the entity-centric pipeline. This bond influnces how we do

evaluation.

To achieve this, we use recall percentages in the filtering stage for

the different choices of entity profiles. However, we use the overall

performance to select the best entity profiles.To generate the overall

pipeline performance we use the official TREC KBA evaluation metric

and scripts \cite{frank2013stream} to report max-F, the maximum

F-score obtained over all relevance cut-offs.