diff --git a/mypaper-final.tex b/mypaper-final.tex
index 30acd959d9384dc6d04b4287d2eb44d31630ce8b..8c1aaf06e815fd4f97c199ab2b5610a60842aca1 100644
--- a/mypaper-final.tex
+++ b/mypaper-final.tex
@@ -214,9 +214,9 @@ The rest of the paper is is organized as follows:
 \textbf{TODO!!}
 
  \section{Data Description}
-We base this analysis on the TREC-KBA 2013 dataset
-\footnote{http://http://trec-kba.org/trec-kba-2013.shtml}. This dataset
-consists of three main parts: a time-stamped stream corpus, a set of
+We base this analysis on the TREC-KBA 2013 dataset%
+\footnote{http://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.
@@ -286,34 +286,33 @@ that can be useful for initial KB-dossier are annotated as
 %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, there are 24162 vital or relevant unique entity-document
-pairs. 9521 of them are vital  and  17424 are relevant. These
-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}
- 
+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
- reprsented persistent profiles  unlike the traditional search system
+ 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
@@ -324,27 +323,27 @@ grouped as others.
  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
+
+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 are the vital(relevant) documents that are not retrievable by a system?
-\end{enumerate}
+ 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
@@ -361,14 +360,14 @@ 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
+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}
+\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. 
