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%        \affaddr{Institute for Clarity in Documentation}\\

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\maketitle

\begin{abstract}

Cumulative citation recommendation refers to the problem faced by

knowledge base curators, who need to continuously screen the media for

updates regarding the knowledge base entries they manage. Automatic

system support for this entity-centric information processing problem

requires complex pipe\-lines involving both natural language

processing and information retrieval components. The pipeline

encountered in a variety of systems that approach this problem

involves four stages: filtering, classification, ranking (or scoring),

and evaluation. Filtering is only an initial step, that reduces the

web-scale corpus of news and other relevant information sources that

may contain entity mentions into a working set of documents that should

be more manageable for the subsequent stages.

Nevertheless, this step has a large impact on the recall that can be

maximally attained! Therefore, in this study, we have focused on just

this filtering stage and conduct an in-depth analysis of the main design

decisions here: how to cleans the noisy text obtained online, 

the methods to create entity profiles, the

types of entities of interest, document type, and the grade of

relevance of the document-entity 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 by examing their contents. This way, we

estimate a practical upper-bound of recall for entity-centric stream

filtering.  

\end{abstract}

% A category with the (minimum) three required fields

\category{H.4}{Information Filtering}{Miscellaneous}

%A category including the fourth, optional field follows...

%\category{D.2.8}{Software Engineering}{Metrics}[complexity measures, performance measures]

\terms{Theory}

\keywords{Information Filtering; Cumulative Citation Recommendation; knowledge maintenance; Stream Filtering;  emerging entities} % NOT required for Proceedings

\section{Introduction}

In 2012, the Text REtrieval Conferences (TREC) introduced the Knowledge Base Acceleration (KBA) track  to help Knowledge Bases(KBs) curators. The track is crucial to address a critical need of KB curators: given KB (Wikipedia or Twitter) entities, filter  a stream  for relevant documents, rank the retrieved documents and recommend them to the KB curators. The track is crucial and timely because  the number of entities in a KB on one hand, and the huge amount of new information content on the Web on the other hand make the task of manual KB maintenance challenging.   TREC KBA's main task, Cumulative Citation Recommendation (CCR), aims at filtering a stream to identify   citation-worthy  documents, rank them,  and recommend them to KB curators.

 Filtering is a crucial step in CCR for selecting a potentially

 relevant set of working documents for subsequent steps of the

 pipeline out of a big collection of stream documents. 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}. 

In the specific setting of CCR, these profiles are

represented by persistent KB entities (Wikipedia pages or Twitter

users, in the TREC scenario).

 TREC-KBA 2013's participants applied Filtering as a first step  to

 produce a smaller working set for subsequent experiments. As the

 subsequent steps of the pipeline use the output of the filter, the

 final performance of the system is dependent on this step.  The

 filtering step particularly determines the recall of the overall

 system. However, all 141 runs submitted by 13 teams did suffer from

 poor recall, as pointed out in the track's overview paper 

 \cite{frank2013stream}. 

The most important components of the filtering step are cleansing

(referring to pre-processing noisy web text into a canonical ``clean''

text format), and

entity profiling (creating a representation of the entity that can be

used to match the stream documents to). For each component, different

choices can be made. In the specific case of TREC KBA, organisers have

provided two different versions of the corpus: one that is already cleansed,

and one that is the raw data as originally collected by the organisers. 

Also, different

approaches use different entity profiles for filtering, varying from

using just the KB entities' canonical names to looking up DBpedia name

variants, and from using the bold words in the first paragraph of the Wikipedia

entities’ page to using anchor texts from other Wikipedia pages, and from

using the exact name as given to WordNet derived synonyms. The type of entities

(Wikipedia or Twitter) and the category of documents in which they

occur (news, blogs, or tweets) cause further variations.

% A variety of approaches are employed  to solve the CCR

% challenge. Each participant reports the steps of the pipeline and the

% 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 is organized as follows: 

\textbf{TODO!!}

 \section{Data Description}

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}},

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

spinn3r\footnote{\url{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 }

\begin{center}

 \begin{tabular}{l*{4}{l}l}

 documents     &   chunk files    &    Sub-stream \\

\hline

126,952         &11,851         &arxiv \\

394,381,405      &   688,974        & social \\

134,933,117       &  280,658       &  news \\

5,448,875         &12,946         &linking \\

57,391,714         &164,160      &   MAINSTREAM\_NEWS (spinn3r)\\

36,559,578         &85,769      &   FORUM (spinn3r)\\

14,755,278         &36,272     &    CLASSIFIED (spinn3r)\\

52,412         &9,499         &REVIEW (spinn3r)\\

7,637         &5,168         &MEMETRACKER (spinn3r)\\

1,040,520,595   &      2,222,554 &        Total\\

\end{tabular}

\end{center}

\label{tab:streams}

\end{table}

\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).  

In total, the dataset contains 24162 unique entity-document

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}

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. 

These extraction results are summarized in Table

\ref{tab:sources}.

For the Twitter entities, we visit

their respective Twitter pages and fetch their display names. 

\begin{table}

\caption{Number of different DBpedia name variants}

\begin{center}

 \begin{tabular}{l*{4}{c}l}

 Name variant& No. of strings  \\

\hline

 Name  &82\\

 Label   &121\\

Redirect  &49 \\

 Birth Name &6\\

 Nickname & 1&\\

 Alias &1 \\

 Alternative Names &4\\

\hline

\end{tabular}

\end{center}

\label{tab:sources}

\end{table}

The collection contains a total number of 121 Wikipedia entities.

Every entity has a corresponding DBpedia label.  Only 82 entities have

a name string and only 49 entities have redirect strings. (Most of the

entities have only one string, except for a few cases with multiple

redirect strings; Buddy\_MacKay, has the highest (12) number of

redirect strings.) 

We combine the different name variants we extracted to form a set of

strings for each KB entity. For Twitter entities, we used the display

names that we collected. 

We consider the names of the entities that

are part of the URL as canonical. For example in entity\\

\url{http://en.wikipedia.org/wiki/Benjamin_Bronfman}\\

Benjamin Bronfman is a canonical name of the entity. From the combined name variants and

the canonical names, we  created four sets of profiles for each

entity: canonical(cano) canonical partial (cano-part), all name

variants combined (all) and partial names of all name

variants(all-part). We refer to the last two profiles as name-variant

and name-variant partial. The names in parentheses are used in table

captions.

\subsection{Annotation Corpus}

The annotation set is a combination of the annotations from before the Training Time Range(TTR) and Evaluation Time Range (ETR) and consists of 68405 annotations.  Its breakdown into training and test sets is  shown in Table \ref{tab:breakdown}.

\begin{table}

\caption{Number of annotation documents with respect to different categories(relevance rating, training and testing)}

\begin{center}

\begin{tabular}{l*{3}{c}r}

 &&Vital&Relevant  &Total \\

\hline

\multirow{2}{*}{Training}  &Wikipedia & 1932  &2051& 3672\\

			  &Twitter&189   &314&488 \\

			   &All Entities&2121&2365&4160\\

\hline 

\multirow{2}{*}{Testing}&Wikipedia &6139   &12375 &16160 \\

                         &Twitter&1261   &2684&3842  \\

                         &All Entities&7400   &12059&20002 \\

             \hline 

\multirow{2}{*}{Total} & Wikipedia       &8071   &14426&19832  \\

                       &Twitter  &1450  &2998&4330  \\

                       &All Entities&9521   &17424&24162 \\

\hline

\end{tabular}

\end{center}

\label{tab:breakdown}

\end{table}

%Most (more than 80\%) of the annotation documents are in the test set.

The 2013 training and test data contain 68405

annotations, of which 50688 are unique document-entity pairs.   Out of

these, 24162 unique document-entity pairs are vital (9521) or relevant

(17424).

\section{Experiments and Results}

 We conducted experiments to study  the effect of cleansing, different entity profiles, types of entities, category of documents, relevance ranks (vital or relevant), and the impact on classification.  In the following subsections, we present the results in different categories, and describe them.