diff --git a/mypaper-final.tex b/mypaper-final.tex
index d85f86c9bb90a36f370d5ee68e37fb5168db177d..c35b576841de63d7dccd375e50472815ac1513dd 100644
--- a/mypaper-final.tex
+++ b/mypaper-final.tex
@@ -65,7 +65,7 @@
 % 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*
+\numberofauthors{2} %  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.
 %
@@ -961,9 +961,7 @@ There is a trade-off between using a richer entity-profile and retrieval of irre
 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
+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
@@ -981,7 +979,7 @@ transformation and cleasing processes.
 
 %%%% NEEDS WORK:
 
-2) Taking both performance (recall at filtering and overall F-score
+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. 
 
 Wikipedia's canonical partial is the best entity profile for Wikipedia entities. This is interesting  to see that the retrieval of of  thousands vital-relevant document-entity pairs by name-variant partial does not translate to an increase in over all performance. It is even more interesting since canonical partial was not considered as contending profile for stream filtering by any of participant to the best of our knowledge. With this understanding, there  is actually no need to go and fetch different names variants from DBpedia, a saving of time and computational resources.
