% THIS IS SIGPROC-SP.TEX - VERSION 3.1

% WORKS WITH V3.2SP OF ACM_PROC_ARTICLE-SP.CLS

% APRIL 2009

%

% It is an example file showing how to use the 'acm_proc_article-sp.cls' V3.2SP

% LaTeX2e document class file for Conference Proceedings submissions.

% ----------------------------------------------------------------------------------------------------------------

% This .tex file (and associated .cls V3.2SP) *DOES NOT* produce:

%       1) The Permission Statement

%       2) The Conference (location) Info information

%       3) The Copyright Line with ACM data

%       4) Page numbering

% ---------------------------------------------------------------------------------------------------------------

% It is an example which *does* use the .bib file (from which the .bbl file

% is produced).

% REMEMBER HOWEVER: After having produced the .bbl file,

% and prior to final submission,

% you need to 'insert'  your .bbl file into your source .tex file so as to provide

% ONE 'self-contained' source file.

%

% Questions regarding SIGS should be sent to

% Adrienne Griscti ---> griscti@acm.org

%

% Questions/suggestions regarding the guidelines, .tex and .cls files, etc. to

% Gerald Murray ---> murray@hq.acm.org

%

% For tracking purposes - this is V3.1SP - APRIL 2009

\documentclass{acm_proc_article-sp}

\usepackage{booktabs}

\usepackage{multirow}

\usepackage{todonotes}

\begin{document}

\title{Entity-Centric Stream Filtering and ranking: Filtering and Unfilterable Documents 

}

%SUGGESTION:

%\title{The Impact of Entity-Centric Stream Filtering on Recall and

%  Missed Documents}

%

% You need the command \numberofauthors to handle the 'placement

% and alignment' of the authors beneath the title.

%

% For aesthetic reasons, we recommend 'three authors at a time'

% i.e. three 'name/affiliation blocks' be placed beneath the title.

%

% NOTE: You are NOT restricted in how many 'rows' of

% "name/affiliations" may appear. We just ask that you restrict

% the number of 'columns' to three.

%

% Because of the available 'opening page real-estate'

% we ask you to refrain from putting more than six authors

% (two rows with three columns) beneath the article title.

% More than six makes the first-page appear very cluttered indeed.

%

% Use the \alignauthor commands to handle the names

% and affiliations for an 'aesthetic maximum' of six authors.

% Add names, affiliations, addresses for

% the seventh etc. author(s) as the argument for the

% \additionalauthors command.

% These 'additional authors' will be output/set for you

% without further effort on your part as the last section in

% the body of your article BEFORE References or any Appendices.

\numberofauthors{8} %  in this sample file, there are a *total*

% of EIGHT authors. SIX appear on the 'first-page' (for formatting

% reasons) and the remaining two appear in the \additionalauthors section.

%

% \author{

% % You can go ahead and credit any number of authors here,

% % e.g. one 'row of three' or two rows (consisting of one row of three

% % and a second row of one, two or three).

% %

% % The command \alignauthor (no curly braces needed) should

% % precede each author name, affiliation/snail-mail address and

% % e-mail address. Additionally, tag each line of

% % affiliation/address with \affaddr, and tag the

% % e-mail address with \email.

% %

% % 1st. author

% \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

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%

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

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

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

pairs, vital or relevant; 9521 of these have been labelled as vital,

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\% mainstream-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}

 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

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

 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

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}

There has been a great deal of interest  as of late on entity-based filtering and ranking. One 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}

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

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.

 \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}

If we look at the recall performances for the raw corpus,   filtering documents by canonical names achieves a recall of  59\%.  Adding other name variants  improves the recall to 79.8\%, an increase of 20.8\%. This means  20.8\% of documents mentioned the entities by other names  rather than by their canonical names. Canonical partial  achieves a recall of 72\%  and name-variant partial achives 90.2\%. This says that 18.2\% of documents mentioned the entities by  partial names of other non-canonical name variants. 

%\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\\

&cano part &94.7& 68.5& 67.8& 99.6& 97.3& 77.3& 10.6& 2.2& 6.0\\

&all & 95.8& 90.1& 72.9& 97.6& 95.1& 73.1& 65.2& 78.4& 72.0\\

&all part &98.8& 95.5& 83.7& 99.7& 98.0& 84.1& 83.3& 89.7& 81.0\\

	                 \hline

\multirow{4}{*}{total} 	&cano    &   81.1& 56.5& 58.2& 87.7& 76.4& 65.7& 9.8& 3.6& 13.5\\

&cano part &92.0& 72.0& 70.6& 99.6& 97.7& 80.1& 9.8& 3.6& 13.5\\

&all & 94.8& 87.1& 75.2& 96.8& 95.3& 75.8& 73.5& 65.4& 71.1\\

&all part & 99.2& 96.8& 86.6& 99.8& 98.4& 86.8& 92.4& 92.7& 84.9\\

\hline

\end{tabular}

\end{center}

\label{tab:source-delta}

\end{table*}

The break down of the raw corpus by document source category is presented in Table

\ref{tab:source-delta}.  

 \subsection{ Relevance Rating: vital and relevant}

When comparing recall for vital and relevant, we observe that

canonical names are more effective for vital than for relevant

entities, in particular for the Wikipedia entities. 

%For example, the recall for news is 80.1 and for social is 76, while the corresponding recall in relevant is 75.6 and 63.2 respectively.

We conclude that the most relevant documents mention the

entities by their common name variants.

%  \subsection{Difference by document categories}

%  

%  Generally, there is greater variation in relevant rank than in vital. This is specially true in most of the Delta's for Wikipedia. This  maybe be explained by news items referring to  vital documents by a some standard name than documents that are relevant. Twitter entities show greater deltas than Wikipedia entities in both vital and relevant. The greater variation can be explained by the fact that the canonical name of Twitter entities retrieves very few documents. The deltas that involve canonical names of Twitter entities, thus, show greater deltas.  

%  

% If we look in recall performances, In Wikipedia entities, the order seems to be others, news and social. This means that others achieve a higher recall than news than social.  However, in Twitter entities, it does not show such a strict pattern. In all, entities also, we also see almost the same pattern of other, news and social. 

\subsection{Recall across document categories: others, news and social}

The recall for Wikipedia entities in Table \ref{tab:name} ranged from

61.8\% (canonicals) to 77.9\% (name-variants).  Table

\ref{tab:source-delta} shows how recall is distributed across document

categories. For Wikipedia entities, across all entity profiles, others

have a higher recall followed by news, and then by social.  While the

recall for news ranges from 76.4\% to 98.4\%, the recall for social

documents ranges from 65.7\% to 86.8\%. In Twitter entities, however,

the pattern is different. In canonicals (and their partials), social

documents achieve higher recall than news.

%This indicates that social documents refer to Twitter entities by their canonical names (user names) more than news do. In name- variant partial, news achieve better results than social. The difference in recall between canonicals and name-variants show that news do not refer to Twitter entities by their user names, they refer to them by their display names.

Overall, across all entities types and all entity profiles, documents

in the others category achieve a higher recall than news, and news documents, in turn, achieve higher recall than social documents. 

% This suggests that social documents are the hardest  to retrieve.  This  makes sense since social posts such as tweets and blogs are short and are more likely to point to other resources, or use short informal names.

%%NOTE TABLE REMOVED:\\\\

%

%We computed four percentage increases in recall (deltas)  between the

%different entity profiles (Table \ref{tab:source-delta2}). The first

%delta is the recall percentage between canonical partial  and

%canonical. The second  is  between name= variant and canonical. The

%third is the difference between name-variant partial  and canonical

%partial and the fourth between name-variant partial and

%name-variant. we believe these four deltas offer a clear meaning. The

%delta between name-variant and canonical means the percentage of

%documents that the new name variants retrieve, but the canonical name

%does not. Similarly, the delta between  name-variant partial and

%partial canonical-partial means the percentage of document-entity

%pairs that can be gained by the partial names of the name variants. 

% The  biggest delta  observed is in Twitter entities between partials

% of all name variants and partials of canonicals (93\%). delta. Both

% of them are for news category.  For Wikipedia entities, the highest

% delta observed is 19.5\% in cano\_part - cano followed by 17.5\% in

% all\_part in relevant. 

  \subsection{Entity Types: Wikipedia and Twitter}

Table \ref{tab:name} summarizes the differences between Wikipedia and

Twitter entities.  Wikipedia entities' canonical representation

achieves a recall of 70\%, while canonical partial achieves a recall of 86.1\%. This is an

increase in recall of 16.1\%. By contrast, the increase in recall of

name-variant partial over name-variant is 8.3\%.

%This high increase in recall when moving from canonical names to their

%partial names, in comparison to the lower increase when moving from

%all name variants to their partial names can be explained by

%saturation: documents have already been extracted by the different

%name variants and thus using their partial names do not bring in many

%new relevant documents.

For Wikipedia entities, canonical

partial achieves better recall than name-variant in both the cleansed and

the raw corpus.  %In the raw extraction, the difference is about 3.7.

and canonical partial are the same for Twitter entities because they

are one word strings. For example in https://twitter.com/roryscovel,

%The low recall is because the canonical names of Twitter entities are

%not really names; they are usually arbitrarily created user names. It

%shows that  documents  refer to them by their display names, rarely

%by their user name, which is reflected in the name-variant recall

%(67.9\%). The use of name-variant partial increases the recall to

%88.2\%.

%% TODO: PERHAPS SUMMARY OF DISCUSSION HERE

\section{Impact on classification}

This is a mapping of strings to Wikipedia concepts and vice versa

\cite{spitkovsky2012cross}. 

(1) the probability with which a string is used as anchor text to

a Wikipedia entity 

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

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

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

For each entity, we computed a PPR score from

a Wikipedia snapshot  and we kept the top 100  entities along

with the corresponding scores.

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

are redirects, labels and names.

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.

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

  body 

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

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

  body normalised by the document length 

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

  Features we use incude similarity features such as cosine and jaccard, document-entity features such as docuemnt mentions entity in title, in body, frequency  of mention, etc., and related entity features such as page rank scores. In total we sue  The features consist of similarity measures between the KB entiities profile text, document-entity features such as  

  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\\