% (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.

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

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

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% \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).

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

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%%%%% MOVED FROM LATER ON - CHECK FLOW

There is a 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. 

%%%%%%%%%%%%

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.  

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

gains. This observation implies that cleansing removes relevant and

social documents than it does vital and news. That it removes relevant

documents more than vital can be explained by the fact that cleansing

removes the related links and adverts which may contain a mention of

the entities. One example we saw was the the cleansing removed an

image with a text of an entity name which was actually relevant. And

that it removes social documents can be explained by the fact that

most of the missing of the missing  docuemnts from cleansed are

social. And all the docuemnts that are missing from raw corpus

social. So in both cases socuial seem to suffer from text

transformation and cleasing processes. 

%%%% NEEDS WORK:

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.

%%%%%%%%%%%%

The deltas between entity profiles, relevance ratings, and document categories reveal four differences between Wikipedia and Twitter entities. 1) For Wikipedia entities, the difference between canonical partial and canonical is higher(16.1\%) than between name-variant partial and  name-variant(8.3\%).  This can be explained by saturation. This is to mean that documents have already been extracted by  name-variants and thus using their partials does not bring in many new relevant documents.  2) Twitter entities are mentioned by name-variant or name-variant partial and that is seen in the high recall achieved  compared to the low recall achieved by canonical(or their partial). This indicates that documents (specially news and others) almost never use user names to refer to Twitter entities. Name-variant partials are the best entity profiles for Twitter entities. 3) However, comparatively speaking, social documents refer to Twitter entities by their user names than news and others suggesting a difference in 

adherence to standard in names and naming. 4) Wikipedia entities achieve higher recall and higher overall performance.