diff --git a/mypaper-final.tex b/mypaper-final.tex
index 904388e7145e9e01db27d8c35f515e1aeb1c303d..30acd959d9384dc6d04b4287d2eb44d31630ce8b 100644
--- a/mypaper-final.tex
+++ b/mypaper-final.tex
@@ -128,7 +128,7 @@ 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 give
+the filtering stage by examing their contents. This way, we
 estimate a practical upper-bound of recall for entity-centric stream
 filtering.  
 
@@ -144,37 +144,106 @@ filtering.
 \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.
+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}. Adaptive Filtering, one task of the filtering track,  starts with   a persistent user profile and a very small number of positive examples. The  filtering step used in CCR systems fits under adaptive filtering: the profiles are represented by persistent KB (Wikipedia or Twitter) entities and there is a small set of relevance judgments representing positive examples. 
+ 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 important step.  The filtering step particularly determines the recall of the overall system. However, all submitted systems suffered from poor recall \cite{frank2013stream}.  The most important components of the filtering step are cleansing, and entity profiling. Each component has choices to make. For example, there are two versions of corpus: cleansed and raw. Different approaches used different entity profiles for filtering. These entity profiles varied from  KB entities' canonical names, to  DBpedia name variants, to using bold words in the first paragraph of the Wikipedia entities’ profiles and anchor texts from other Wikipedia pages, to using exact name and wordNet synonyms. Moreover, the Type of entities (Wikipedia or Twitter), the category of 
-documents (news, blog, tweets) can influence filtering.
+ 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.
  
-
- 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 hold the subsequent steps of the pipeline fixed, zoom in on the filtering step and  conduct an in-depth analysis of the main components in it.  In particular, we study  cleansing, different entity profiling,  type of entities (Wikipedia or Twitter), and document categories (social, news, etc).  The main contribution of the paper are an in-depth analysis of the factors that affect entity-based stream filtering, identifying optimal entity profiles vis-avis not 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: 
- 
- 
- 
-
+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 use the TREC-KBA 2013 dataset \footnote{http://http://trec-kba.org/trec-kba-2013.shtml}. The dataset consists of a time-stamped  stream corpus, a set of KB entities, and a set of relevance judgments. 
-\subsection{Stream corpus} The stream corpus comes in two versions: raw and cleaned. The raw  and cleansed versions are 6.45TB and 4.5TB respectively,  after xz-compression and GPG encryption. The raw data is a  dump of  raw HTML pages. The cleansed version is the raw data after its HTML tags are stripped off and only English documents identified with Chromium Compact Language Detector \footnote{https://code.google.com/p/chromium-compact-language-detector/} are included.  The stream corpus is organized in hourly folders each of which contains many  chunk files. Each chunk file contains between hundreds and hundreds of thousands of serialized  thrift objects. One thrift object is one document. A document could be a blog article, a news article, or a social media post (including tweet).  The stream corpus comes from three sources: TREC KBA 2012 (social, news and linking) \footnote{http://trec-kba.org/kba-stream-corpus-2012.shtml}, arxiv\footnote{http://arxiv.org/}, and spinn3r\footnote{http://spinn3r.com/}. 
-Table \ref{tab:streams}   shows the sources, the number of hourly directories, and the number of chunk files. 
-
+We base this analysis on the TREC-KBA 2013 dataset
+\footnote{http://http://trec-kba.org/trec-kba-2013.shtml}. This dataset
+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{https://code.google.com/p/chromium-compact-language-detector/}
+are included.  The stream corpus is organized in hourly folders each
+of which contains many  chunk files. Each chunk file contains between
+hundreds and hundreds of thousands of serialized  thrift objects. One
+thrift object is one document. A document could be a blog article, a
+news article, or a social media post (including tweet).  The stream
+corpus comes from three sources: TREC KBA 2012 (social, news and
+linking) \footnote{http://trec-kba.org/kba-stream-corpus-2012.shtml},
+arxiv\footnote{http://arxiv.org/}, and
+spinn3r\footnote{http://spinn3r.com/}.
+Table \ref{tab:streams} shows the sources, the number of hourly
+directories, and the number of chunk files.
 \begin{table}
 \caption{Retrieved documents to different sources }
 \begin{center}
 
- \begin{tabular}{r*{4}{r}l}
+ \begin{tabular}{l*{4}{l}l}
  documents     &   chunk files    &    Sub-stream \\
 \hline
 
@@ -200,8 +269,18 @@ Table \ref{tab:streams}   shows the sources, the number of hourly directories, a
 
 \subsection{Relevance judgments}
 
-TREC-KBA provided relevance judgments for training and testing. Relevance judgments are given to 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. 
-
+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
@@ -230,12 +309,35 @@ 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 reprsented 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.  
- 
+ 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
+ reprsented 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: given a stream of documents of news items, blogs and social media on one hand and KB entities  on the other, 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}
+ Stream filtering: given a stream of documents of news items, blogs
+ and social media on one hand and KB entities  on the other, 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?
@@ -243,12 +345,29 @@ grouped as others.
   \item Does a gain in recall at filtering step translate to a gain in F-measure at the end of the pipeline?
   \item What are the vital(relevant) documents that are not retrievable by a system?
 \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. 
-
-
+
+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.
 
