diff --git a/mypaper-final.tex b/mypaper-final.tex
index 0e2e11ce283a60bfb16292f3423cd05fd6b5de30..2eba700899bd42dc857617d114790236d49c9fe3 100644
--- a/mypaper-final.tex
+++ b/mypaper-final.tex
@@ -111,17 +111,18 @@ 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 default pipeline
+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 an initial step, that reduces the
+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.
-This step has a large impact on the recall that can be achieved.
-Keeping the subsequent steps constant, we therefore zoom in into the
-filtering stage, and conduct an in-depth analysis of the main design
-decisions here:
-cleansing noisy web data, the methods to create entity profiles, the
+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
