% \alignauthor

% Ben Trovato\titlenote{Dr.~Trovato insisted his name be first.}\\

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

%        \affaddr{1932 Wallamaloo Lane}\\

%        \affaddr{Wallamaloo, New Zealand}\\

%        \email{trovato@corporation.com}

% % 2nd. author

% \alignauthor

% G.K.M. Tobin\titlenote{The secretary disavows

% any knowledge of this author's actions.}\\

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

%        \affaddr{P.O. Box 1212}\\

%        \affaddr{Dublin, Ohio 43017-6221}\\

%        \email{webmaster@marysville-ohio.com}

% }

% There's nothing stopping you putting the seventh, eighth, etc.

% author on the opening page (as the 'third row') but we ask,

% for aesthetic reasons that you place these 'additional authors'

% in the \additional authors block, viz.

% Just remember to make sure that the TOTAL number of authors

% is the number that will appear on the first page PLUS the

% number that will appear in the \additionalauthors section.

\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

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. Filtering  sifts  an incoming information for information relevant to user 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 contributions 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

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

present the experimental results 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 documents in

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

\ref{sec:conc}.

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

Our study is carried out using 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''. The chunk files contain

hunderds up to hundreds of thousands of semi-structured documents,

serialized as thrift objects (one thrift object corresponding to one

document). A document could be a blog article, a

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

corpus has been derived from three main sources: TREC KBA 2012

(containing blogs, news and web pages with shortened urls)%

\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 in the dataset consist of 20 Twitter entities and 121

Wikipedia entities. These selected entities are, on purpose, ``sparse

entities'' in the sense that they only occur in relatively few

documents. The collection of entities consists of 71 people entities,

1 organization entity, and 24 facilities entities.  

\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

and the remaining 17424 as relevant.

All documents are categorized into 8 source categories: 0.98\%

arxiv(a), 0.034\% classified(c), 0.34\% forum(f), 5.65\% linking(l),

11.53\% main\-stream-news(m-n), 18.40\% news(n), 12.93\% social(s) and

50.2\% weblog(w). We have regrouped these source categories into three

groups ``news'', ``social'', and ``other'', for two reasons: 1) some groups

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}\label{sec:fil}

 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

 represented by persistent profiles, unlike the traditional search system

 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

 examples.

Stream filtering is then the task to, given a stream of documents of news items, blogs

 and social media on one hand and a set of KB entities on the other,

 to filter the stream for  potentially relevant documents  such that

 the relevance classifier(ranker) achieves as maximum performance as

 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

 of entities (Wikipedia or Twitter) , and finally their impact overall

 performance of the pipeline. Finally, we conduct manual examination

 of the vital documents that defy filtering. We strive to answer the

 following research questions:

\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?

  \item What characterizes the vital (and relevant) documents that are

    missed in the filtering step?

\end{enumerate}

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

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

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

as part of the pipeline needs that delicate balance between retrieving

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

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}

 A more recent manifestation of that is the introduction of TREC KBA in 2012.  Following that, there have been a number of research works done on the topic \cite{frank2012building, ceccarelli2013learning, taneva2013gem, wang2013bit, balog2013multi}.  These works are based on KBA 2012 task and dataset  and they address the whole problem of entity filtering and ranking.  TREC KBA continued in 2013, but the task underwent some changes. The main change between  the 2012 and 2013 are in the number of entities, the type of entities, the corpus and the relevance rankings.

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. 

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}

%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}\label{sec:expr}

 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.

 \subsection{Cleansing: raw or cleansed}

\begin{table}

\caption{Percentage of vital or relevant documents retrieved under different name variants (upper part from cleansed, lower part from raw)}

\begin{center}

\begin{tabular}{l@{\quad}rrrrrrr}

\hline

&cano&cano-part  &all &all-part  \\

\hline

   Wikipedia      &61.8  &74.8  &71.5  &77.9\\

   Twitter        &1.9   &1.9   &41.7  &80.4\\

   All Entities   &51.0  &61.7  &66.2  &78.4 \\	

\hline

\hline

   Wikipedia      &70.0  &86.1  &82.4  &90.7\\

   Twitter        & 8.7  &8.7   &67.9  &88.2\\

  All Entities    &59.0  &72.2  &79.8  &90.2\\

\hline

\end{tabular}

\end{center}

\label{tab:name}

\end{table}

The upper part of Table \ref{tab:name} shows the recall performances on the cleansed version and the lower part on the raw version. The recall performances for all entity types  are increased substantially in the raw version. Recall increases on Wikipedia entities  vary from 8.2 to 12.8, and in Twitter entities from 6.8 to 26.2. In all entities, it ranges from 8.0 to 13.6.  The recall increases are substantial. To put it into perspective, an 11.8 increase in recall on all entities is a retrieval of 2864 more unique document-entity pairs. %This suggests that cleansing has removed some documents that we could otherwise retrieve. 

\subsection{Entity Profiles}

%\begin{table*}

%\caption{Breakdown of recall percentage increases by document categories }

%\begin{center}\begin{tabular}{l*{9}{c}r}

% && \multicolumn{3}{ c| }{All entities}  & \multicolumn{3}{ c| }{Wikipedia} &\multicolumn{3}{ c| }{Twitter} \\ 

% & &others&news&social & others&news&social &  others&news&social \\

%\hline

% 

%\multirow{4}{*}{Vital}	 &cano-part $-$ cano  	&8.2  &14.9    &12.3           &9.1  &18.6   &14.1             &0      %&0       &0  \\

%                         &all$-$ cano         	&12.6  &19.7    &12.3          &5.5  &15.8   &8.4             &73   &35%.9    &38.3  \\

%	                 &all-part $-$ cano\_part&9.7    &18.7  &12.7       &0    &0.5  &5.1        &93.2 & 93 &64.4 \\%

%	                 &all-part $-$ all     	&5.4  &13.9     &12.7           &3.6  &3.3    &10.8              &20.3 %  &57.1    &26.1 \\

%	                 \hline

%	                 

%\multirow{4}{*}{Relevant}  &cano-part $-$ cano  	&10.5  &15.1    &12.2          &11.1  &21.7   &14.1            % &0   &0    &0  \\

%                         &all $-$ cano         	&11.7  &36.6    &17.3          &9.2  &19.5   &9.9             &%54.5   &76.3   &66  \\

%	                 &all-part $-$ cano-part &4.2  &26.9   &15.8          &0.2    &0.7    &6.7           &72.2   &8%7.6 &75 \\

%	                 &all-part $-$ all     	&3    &5.4     &10.7           &2.1  &2.9    &11              &18.2   &%11.3    &9 \\

%	                 

%	                 \hline

%\multirow{4}{*}{total} 	&cano-part $-$ cano   	&10.9   &15.5   &12.4         &11.9  &21.3   &14.4          &0 %    &0       &0\\

%			&all $-$ cano         	&13.8   &30.6   &16.9         &9.1  &18.9   &10.2          &63.6  &61.8%    &57.5 \\

%                        &all-part $-$ cano-part	&7.2   &24.8   &15.9          &0.1    &0.7    &6.8           &8%2.2  &89.1    &71.3\\

%                        &all-part $-$ all     	&4.3   &9.7    &11.4           &3.0  &3.1   &11.0          &18.9  &27.3%    &13.8\\	                 

%	                 

%                                  	                 

%\hline

%\end{tabular}

%\end{center}

%\label{tab:source-delta2}

%\end{table*}

 \begin{table*}

\caption{Breakdown of recall performances by document source category}

\begin{center}\begin{tabular}{l*{9}{c}r}

 && \multicolumn{3}{ c| }{All entities}  & \multicolumn{3}{ c| }{Wikipedia} &\multicolumn{3}{ c| }{Twitter} \\ 

 & &others&news&social & others&news&social &  others&news&social \\

\hline

\multirow{4}{*}{Vital} &cano                 &82.2& 65.6& 70.9& 90.9&  80.1& 76.8&   8.1&  6.3&  30.5\\

&cano part & 90.4& 80.6& 83.1& 100.0& 98.7& 90.9&   8.1&  6.3&  30.5\\

&all  & 94.8& 85.4& 83.1& 96.4&  95.9& 85.2&   81.1& 42.2& 68.8\\

&all part &100& 99.2& 95.9& 100.0&  99.2& 96.0&   100&  99.3& 94.9\\

\hline

\multirow{4}{*}{Relevant} &cano & 84.2& 53.4& 55.6& 88.4& 75.6& 63.2& 10.6& 2.2& 6.0\\

a Wikipedia entity 

%thus distributing the probability mass over the different entities that it is used as anchor text;

and (2) the 

probability that indicates the strength of co-reference of an anchor with respect to other anchors to  a given Wikipedia entity.  We use the product of both for each string.

\paragraph*{jac} 

  Jaccard similarity between the document and the entity's Wikipedia page

\paragraph*{cos} 

  Cosine similarity between the document and the entity's Wikipedia page

\paragraph*{kl} 

  KL-divergence between the document and the entity's Wikipedia page

  \paragraph*{PPR}

For each entity, we computed a PPR score from

a Wikipedia snapshot  and we kept the top 100  entities along

with the corresponding scores.

\paragraph*{Surface Form (sForm)}

For each Wikipedia entity, we gathered DBpedia name variants. These

are redirects, labels and names.

\paragraph*{Context (contxL, contxR)}

From the WikiLink corpus \cite{singh12:wiki-links}, we collected

all left and right contexts (2 sentences left and 2 sentences

right) and generated n-grams between uni-grams and quadro-grams

for each left and right context. 

Finally,  we select  the 5 most frequent n-grams for each context.

\paragraph*{FirstPos}

  Term position of the first occurrence of the target entity in the document 

  body 

\paragraph*{LastPos }

  Term position of the last occurrence of the target entity in the document body

\paragraph*{LengthBody} Term count of document body

\paragraph*{LengthAnchor} Term count  of document anchor

\paragraph*{FirstPosNorm} 

  Term position of the first occurrence of the target entity in the document 

  body normalised by the document length 

\paragraph*{MentionsBody }

  No. of occurrences of the target entity in the  document body

  In here, we present results showing how  the choices in corpus, entity types, and entity profiles impact these latest stages of the pipeline.  In tables \ref{tab:class-vital} and \ref{tab:class-vital-relevant}, we show the performances in max-F. 

\begin{table*}

\caption{vital performance under different name variants(upper part from cleansed, lower part from raw)}

\begin{center}

\begin{tabular}{ll@{\quad}lllllll}

\hline

%&\multicolumn{1}{l}{\rule{0pt}{12pt}}&\multicolumn{1}{l}{\rule{0pt}{12pt}cano}&\multicolumn{1}{l}{\rule{0pt}{12pt}canonical partial }&\multicolumn{1}{l}{\rule{0pt}{12pt}name-variant }&\multicolumn{1}{l}{\rule{0pt}{50pt}name-variant partial}\\[5pt]

  &&cano&cano-part&all  &all-part \\

   all-entities &max-F& 0.241&0.261&0.259&0.265\\

%	      &SU&0.259  &0.258 &0.263 &0.262 \\	

   Wikipedia &max-F&0.252&0.274& 0.265&0.271\\

%	      &SU& 0.261& 0.259&  0.265&0.264 \\

   twitter &max-F&0.105&0.105&0.218&0.228\\

%     &SU &0.105&0.250& 0.254&0.253\\

\hline

\hline

  all-entities &max-F & 0.240 &0.272 &0.250&0.251\\

%	  &SU& 0.258   &0.151  &0.264  &0.258\\

   Wikipedia&max-F &0.257&0.257&0.257&0.255\\

%   &SU	     & 0.265&0.265 &0.266 & 0.259\\

   twitter&max-F &0.188&0.188&0.208&0.231\\

%	&SU&    0.269 &0.250 &0.250&0.253\\

\hline

\end{tabular}

\end{center}

\label{tab:class-vital}

\end{table*}

  \begin{table*}

\caption{vital-relevant performances under different name variants(upper part from cleansed, lower part from raw)}

\begin{center}

\begin{tabular}{ll@{\quad}lllllll}

\hline

%&\multicolumn{1}{l}{\rule{0pt}{12pt}}&\multicolumn{1}{l}{\rule{0pt}{12pt}canonical}&\multicolumn{1}{l}{\rule{0pt}{12pt}canonical partial }&\multicolumn{1}{l}{\rule{0pt}{12pt}name-variant }&\multicolumn{1}{l}{\rule{0pt}{50pt}name-variant partial}\\[5pt]

 &&cano&cano-part&all  &all-part \\

   all-entities &max-F& 0.497&0.560&0.579&0.607\\

%	      &SU&0.468  &0.484 &0.483 &0.492 \\	

   Wikipedia &max-F&0.546&0.618&0.599&0.617\\

ranges from  6.8\% t 26.2\%. These increases, in actual

document-entity pairs,  is in thousands. We believe this is a

substantial increase. However, the recall increases do not always

translate to improved F-score in overall performance.  In the vital

relevance ranking for both Wikipedia and aggregate entities, the

cleansed version performs better than the raw version.  In Twitter

entities, the raw corpus achieves better except in the case of all

name-variant, though the difference is negligible.  However, for

vital-relevant, the raw corpus performs  better across all entity

profiles and entity types except in partial canonical names of

Wikipedia entities.

The use of different profiles also shows a big difference in

recall. While in Wikipedia the use of canonical

partial achieves better than name-variant, there is a steady increase

in recall from canonical to canonical partial, to name-variant, and

to name-variant partial. This pattern is also observed across the

document categories.  However, here too, the relationship between

the gain in recall as we move from less richer profile to a more

richer profile and overall performance as measured by F-score  is not

linear.

%%%%%%%%%%%%

In vital ranking, across all entity profiles and types of corpus,

Wikipedia's canonical partial  achieves better performance than any

other Wikipedia entity profiles. In vital-relevant documents too,

Wikipedia's canonical partial achieves the best result. In the raw

corpus, it achieves a little less than name-variant partial. For

Twitter entities, the name-variant partial profile achieves the

highest F-score across all entity profiles and types of corpus.

There are 3 interesting observations: 

1) cleansing impacts Twitter

entities and relevant documents.  This  is validated by the

observation that recall  gains in Twitter entities and the relevant

categories in the raw corpus also translate into overall performance

gains. This observation implies that cleansing removes relevant and

social documents than it does vital and news. That it removes relevant

documents more than vital can be explained by the fact that cleansing

removes the related links and adverts which may contain a mention of

the entities. One example we saw was the the cleansing removed an

image with a text of an entity name which was actually relevant. And

that it removes social documents can be explained by the fact that

social. So in both cases social seem to suffer from text

%%%% NEEDS WORK:

Taking both performance (recall at filtering and overall F-score

during evaluation) into account, there is a clear trade-off between

using a richer entity-profile and retrieval of irrelevant

documents. The richer the profile, the more relevant documents it

retrieves, but also the more irrelevant documents. To put it into

perspective, lets compare the number of documents that are retrieved

with  canonical partial and with name-variant partial. Using the raw

corpus, the former retrieves a total of 2547487 documents and achieves

a recall of 72.2\%. By contrast, the later retrieves a total of

4735318 documents and achieves a recall of 90.2\%. The total number of

documents extracted increases by 85.9\% for a recall gain of 18\%. The

rest of the documents, that is 67.9\%, are newly introduced irrelevant

documents.

Perhaps surprising, Wikipedia's canonical partial is the best entity profile for Wikipedia

entities. Here, the retrieval of

thousands vital-relevant document-entity pairs by name-variant partial

does not materialize into an increase in over all performance. Notice

that none of the participants in TREC KBA considered canonical partial

as a viable strategy though. We conclude that, at least for our

system, the remainder of the pipeline needs a different approach to

handle the correct scoring of the additional documents -- that are

necessary if we do not want to accept a low recall of the filtering

step.

%With this understanding, there  is actually no

%need to go and fetch different names variants from DBpedia, a saving

%of time and computational resources.

%%%%%%%%%%%%

The deltas between entity profiles, relevance ratings, and document categories reveal four differences between Wikipedia and Twitter entities. 1) For Wikipedia entities, the difference between canonical partial and canonical is higher(16.1\%) than between name-variant partial and  name-variant(8.3\%).  This can be explained by saturation. This is to mean that documents have already been extracted by  name-variants and thus using their partials does not bring in many new relevant documents.  2) Twitter entities are mentioned by name-variant or name-variant partial and that is seen in the high recall achieved  compared to the low recall achieved by canonical(or their partial). This indicates that documents (specially news and others) almost never use user names to refer to Twitter entities. Name-variant partials are the best entity profiles for Twitter entities. 3) However, comparatively speaking, social documents refer to Twitter entities by their user names than news and others suggesting a difference in 

adherence to standard in names and naming. 4) Wikipedia entities achieve higher recall and higher overall performance. 

The high recall and subsequent higher overall performance of Wikipedia entities can  be due to two reasons. 1) Wikipedia entities are relatively well described than Twitter entities. The fact that we can retrieve different name variants from DBpedia is a measure of relatively rich description. Rich description plays a role in both filtering and computation of features such as similarity measures in later stages of the pipeline.   By contrast, we have only two names for Twitter entities: their user names and their display names which we collect from their Twitter pages. 2) There is not DBpedia-like resource for Twitter entities from which alternative names cane be collected.   

In the experimental results, we also observed that recall scores in the vital category are higher than in the relevant category. This observation  confirms one commonly held assumption:(frequency) mention is related to relevance.  this is the assumption why term frequency is used an indicator of document relevance in many information retrieval systems. The more  a document mentions an entity explicitly by name, the more likely the document is vital to the entity.

Across document categories, we observe a pattern in recall of others, followed by news, and then by social. Social documents are the hardest to retrieve. This can be explained by the fact that social documents (tweets and  blogs) are more likely to point to a resource where the entity is mentioned, mention the entities with some short abbreviation, or talk without mentioning the entities, but with some context in mind. By contrast news documents mention the entities they talk about using the common name variants more than social documents do. However, the greater difference in percentage recall between the different entity profiles in the news category indicates news refer to a given entity with different names, rather than by one standard name. By contrast others show least variation in referring to news. Social documents falls in between the two.  The deltas, for Wikipedia entities, between canonical partials and canonicals,  and name-variants and canonicals are high, an indication that canonical partials 

and name-variants bring in new relevant documents that can not be retrieved by canonicals. The rest of the two deltas are very small,  suggesting that partial names of name variants do not bring in new relevant documents. 

%\section{Unfilterable documents}\label{sec:unfil}

\section{Missing vital-relevant documents}\label{sec:unfil}

% 

 The use of name-variant partial for filtering is an aggressive attempt to retrieve as many relevant documents as possible at the cost of retrieving irrelevant documents. However, we still miss about  2363(10\%) of the vital-relevant documents.  Why are these documents missed? If they are not mentioned by partial names of name variants, what are they mentioned by? Table \ref{tab:miss} shows the documents that we miss with respect to cleansed and raw corpus.  The upper part shows the number of documents missing from cleansed and raw versions of the corpus. The lower part of the table shows the intersections and exclusions in each corpus.  

\begin{table}

\caption{The number of documents missing  from raw and cleansed extractions. }

\begin{center}

\begin{tabular}{l@{\quad}llllll}

\hline

\multicolumn{1}{l}{\rule{0pt}{12pt}category}&\multicolumn{1}{l}{\rule{0pt}{12pt}Vital }&\multicolumn{1}{l}{\rule{0pt}{12pt}Relevant }&\multicolumn{1}{l}{\rule{0pt}{12pt}Total }\\[5pt]

\hline

Cleansed &1284 & 1079 & 2363 \\

Raw & 276 & 4951 & 5227 \\

\hline

 missing only from cleansed &1065&2016&3081\\

  missing only from raw  &57 &160 &217 \\

  Missing from both &219 &1927&2146\\

\hline

\end{tabular}

\end{center}

\label{tab:miss}

\end{table}

One would  assume that  the set of document-entity pairs extracted from cleansed are a sub-set of those   that are extracted from the raw corpus. We find that that is not the case. There are 217  unique entity-document pairs that are retrieved from the cleansed corpus, but not from the raw. 57 of them are vital.    Similarly,  there are  3081 document-entity pairs that are missing  from cleansed, but are present in  raw. 1065 of them are vital.  Examining the content of the documents reveals that it is due to a missing part of text from a corresponding document.  All the documents that we miss from the raw corpus are social. These are documents such as tweets and blogs, posts from other social media. To meet the format of the raw data (binary byte array), some of them must have been converted later, after collection and on the way lost a part or the entire content. It is similar for the documents that we miss from cleansed: a part or the entire content  is lost in during the cleansing process (the removal of 

HTML tags and non-English documents).  In both cases the mention of the entity happened to be on the part of the text that is cut out during transformation. 

 The interesting set  of relevance judgments are those that  we miss from both raw and cleansed extractions. These are 2146 unique document-entity pairs, 219 of them are with vital relevance judgments.   The total number of entities in the missed vital annotations is  28 Wikipedia and 7  Twitter, making a total of 35. The  great majority (86.7\%) of the documents are social. This suggests that social (tweets and blogs) can talk about the entities without mentioning  them by name more than news and others do. This is, of course, inline with intuition. 

%%%%%%%%%%%%%%%%%%%%%%

We observed that there are vital-relevant documents that we miss from raw only, and similarly from cleansed only. The reason for this is transformation from one format to another. The most interesting documents are those that we miss from both raw and cleansed corpus. We first identified the number of KB entities who have a vital relevance judgment and  whose documents can not be retrieved (they were 35 in total) and conducted a manual examination into their content to find out why they are missing. 

 We  observed  that among the missing documents, different document ids can have the same content, and be judged multiple times for a given entity.  %In the vital annotation, there are 88 news, and 409 weblog. 

 Avoiding duplicates, we randomly selected 35 documents, one for each entity.   The documents are 13 news and  22  social. Here below we have classified the situation under which a document can be vital for an entity without mentioning the entities with the different entity  profiles we used for filtering. 

\paragraph*{Outgoing link mentions} A post (tweet) with an outgoing link which mentions the entity.

\paragraph*{Entity -related entity} A document about an important figure such as artist, athlete  can be vital to another. This is specially true if the two are contending for the same title, one has snatched a title, or award from the other. 

\paragraph*{Organization - main activity} A document that talks about about an area on which the company is active is vital for the organization. For example, Atacocha is a mining company  and a news item on mining waste was annotated vital. 

\paragraph*{Entity - group} If an entity belongs to a certain group (class),  a news item about the group can be vital for the individual members. FrankandOak is  named innovative company and a news item that talks about the group  of innovative companies is relevant for a  it. Other examples are: a  big event  of which an entity is related such an Film awards for actors. 

\paragraph*{Artist - work} Documents that discuss the work of artists can be relevant to the artists. Such cases include  books or films being vital for the book author or the director (actor) of the film. Robocop is film whose screenplay is by Joshua Zetumer. A blog that talks about the film was judged vital for Joshua Zetumer. 

\paragraph*{Politician - constituency} A major political event in a certain constituency is vital for the politician from that constituency. 

 A good example is a weblog that talks about two north Dakota counties being drought disasters. The news is vital for Joshua Boschee, a politician, a member of North Dakota democratic party.  

\paragraph*{head - organization} A document that talks about an organization of which the entity is the head can be vital for the entity.  Jasper\_Schneider is USDA Rural Development state director for North Dakota and an article about problems of primary health centers in North Dakota is judged vital for him. 

\paragraph*{World Knowledge} Some things are impossible to know without your world knowledge. For example ''refreshments, treats, gift shop specials, "bountiful, fresh and fabulous holiday decor," a demonstration of simple ways to create unique holiday arrangements for any home; free and open to the public`` is judged relevant to Hjemkomst\_Center. This is a social media post, and unless one knows the person posting it, there is no way that this text shows that. Similarly ''learn about the gray wolf's hunting and feeding behaviors and watch the wolves have their evening meal of a full deer carcass; $15 for members, $20 for nonmembers`` is judged vital to Red\_River\_Zoo.  

\paragraph*{No document content} A small number of documents were found to have no content.

\paragraph*{Disagreement} For a few remaining documents, the authors disagree with the assessors as to why these are vital to the entity.

\section{Conclusions} \label{sec:conc}

In this paper, we examined the filtering stage of the entity-centric stream filtering and ranking  by holding the later stages of fixed. In particular, we studied the cleansing step, different entity profiles, type of entities(Wikipedia or Twitter), categories of documents(news, social, or others) and the relevance ratings. We attempted to address the following research questions: 1) does cleansing affect filtering and subsequent performance? 2) what is the most effective way of entity profiling? 3) is filtering different for Wikipedia and Twitter entities? 4) are some type of documents easily filterable and others not? 5) does a gain in recall at filtering step translate to a gain in F-measure at the end of the pipeline? and 6) what are the circumstances under which vital documents can not be retrieved? 

Cleansing does remove parts or entire contents of documents making them irretrievable. However, because of the introduction of false positives, recall gains by  raw corpus and some  richer entity profiles do not necessarily translate to overall performance gain. The  conclusion on this is mixed in the sense that cleansing helps improve the recall on vital documents and Wikipedia entities, but reduces the recall on Twitter entities and the relative category of relevance ranking. Vital and relevant documents show a difference in retrieval performance. Vital documents are easier to filter than relevant.  

Despite an aggressive attempt to filter as many vital-relevant documents as possible,  we observe that there are still documents that we miss. While some are possible to retrieve with some modifications, some others are not. There are some document that indicate that an information filtering system does not seem to get them no matter how rich representation of entities they use. These circumstances under which this happens are many. We found that some documents have no content at all, subjectivity(it is not clear why some are judged vital). However, the main circumstances under which vital  documents can defy filtering is: outgoing link mentions, 

venue-event, entity - related entity, organization - main area of operation, entity - group, artist - artist's work,  party-politician, and world knowledge.  

%ACKNOWLEDGMENTS are optional

%\section{Acknowledgments}

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