% final results in comparison to other systems.  A typical TREC KBA

% poster presentation or talk explains the system pipeline and reports

% the final results. The systems may employ similar (even the same)

% steps  but the choices they make at every step are usually

% different. 

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 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 litrature folowed by a discussion of our method in \ref{sec:mthd}. Following that,  we present the experimental resulsy in \ref{sec:expr}, and discuss and 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 docuemnts 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

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

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.

\section{Literature Review} \label{sec:lit}

The number of entities increased from 29 to 141, and it included 20 Twitter entities. The TREC KBA 2012 corpus is 1.9TB after xz-compression and has  400M documents. By contrast, the KBA 2013 corpus is 6.45 after XZ-compression and GPG encryption. A version with all-non English documented removed  is 4.5 TB and consists of 1 Billion documents. The 2013 corpus subsumed the 2012 corpus and added others from spinn3r, namely main-stream news, forum, arxiv, classified, reviews and meme-tracker.  A more important difference is, however, a change in the definitions of relevance ratings vital and relevant. While in KBA 2012, a document was judged vital if it has citation-worthy content for a given entity, in 2013 it must have the freshliness, that is the content must trigger an editing of the given entity's KB entry. 

While the tasks of 2012 and 2013 are fundamentally the same, the approaches  varied due  to the size of the corpus. In 2013, all participants used filtering to reduce the size of the big corpus.   They used different ways of filtering: many of them used two or more of different name variants from DBpedia such as labels, names, redirects, birth names, alias, nicknames, same-as and alternative names \cite{wang2013bit,dietzumass,liu2013related, zhangpris}.  Although most of the participants used DBpedia name variants none of them used all the name variants.  A few other participants used bold words in the first paragraph of the Wikipedia entity's profiles and anchor texts from other Wikipedia pages  \cite{bouvierfiltering, niauniversity}. One participant used Boolean \emph{and} built from the tokens of the canonical names \cite{illiotrec2013}.  

All of the studies used filtering as their first step to generate a smaller set of documents. And many systems suffered from poor recall and their system performances were highly affected \cite{frank2012building}. Although  systems  used different entity profiles to filter the stream, and achieved different performance levels, there is no study on and the factors and choices that affect the filtering step itself. Of course filtering has been extensively examined in TREC Filtering \cite{robertson2002trec}. However, those studies were isolated in the sense that they were intended to optimize recall. What we have here is a different scenario. Documents have relevance rating. Thus we want to study filtering in connection to  relevance to the entities and thus can be done by coupling filtering to the later stages of the pipeline. This is new to the best of our knowledge and the TREC KBA problem setting and data-sets offer a good opportunity to examine this aspect of filtering. 

Moreover, there has not been a chance to study at this scale and/or a study into what type of documents defy filtering and why? In this paper, we conduct a manual examination of the documents that are missing and classify them into different categories. We also estimate the general upper bound of recall using the different entities profiles and choose the best profile that results in an increased over all performance as measured by F-measure. 

\section{Method}\label{sec:mthd}

All analyses in this paper are carried out on the documents that have

relevance assessments associated to them. For this purpose, we

extracted those documents from the big corpus. We experiment with all

KB entities. For each KB entity, we extract different name variants

from DBpedia and Twitter.

\

\subsection{Entity Profiling}

We build entity profiles for the KB entities of interest. We have two

types: Twitter and Wikipedia. Both entities have been selected, on

purpose by the track organisers, to occur only sparsely and be less-documented.

For the Wikipedia entities, we fetch different name variants

from DBpedia: name, label, birth name, alternative names,

redirects, nickname, or alias. 

  year={2013}

}

@article{niauniversity,

  title={University of Florida Knowledge Base Acceleration Notebook},

  author={Nia, Morteza Shahriari and Grant, Christan and Peng, Yang and Wang, Daisy Zhe and Petrovic, Milenko},

   journal={Proceedings of The 22th TREC},

  year={2013}

}

@article{frank2013stream,

  title={Evaluating Stream Filtering for Entity Profile Updates for TREC 2013},

  author={Frank, John R and Bauer, J and  Kleiman-Weiner, Max and Roberts, Daniel A and Tripuraneni, Nilesh  and  Zhang, Ce and R{\'e}, Christopher and Voohees, Ellen and Soboroff, Ian},

  journal={Proceedings of The 22th TREC},

  year={2013}

}

@inproceedings{spitkovsky2012cross,

  title={A Cross-Lingual Dictionary for English Wikipedia Concepts.},

  author={Spitkovsky, Valentin I and Chang, Angel X},

  booktitle={LREC},

  pages={3168--3175},

  year={2012}

}