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 was 1.9TB after xz-compression and had  400M documents. By contrast, the KBA 2013 corpus was 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, that the definition of the relevance ranking changed. The change   in the definitions of vital and relevant. While in KBA 2012, a document was judged vital if it has citation-worthy content, In 2013 it must have the freshliness, that is the content must trigger an editing of the KB entry. 

While the task of 2012 and 2013 are fundamentally the same, the approaches for the tasks varied due  to the size of the corpus. In the 2013, all participants used filtering to reduce the size of the big corpus.   They used different ways of filtering: many of them used two or more of different name variants from DBpedia such as labels, names, redirects, birth names, alias, nicknames, same-as and alternative names \cite{wang2013bit, dietzumass ,liu2013related, zhangpris}.  Although all of the participants used DBpedia name variants none of them used all them.  A few other participants used bold words in the first paragraph of the Wikipedia entity's profiles and anchor texts from other Wikipedia pages  \cite{bouvierfiltering, niauniversity}.  Very few participants used Boolean And built from the tokens of the canonical names \cite{illiotrec2013}.  

All of the studies mentioned used filtering as their first step to generate a smaller set of documents. And many systems suffered from poor recall and their system performances were highly affected \cite{frank2012building}. Although  systems  used different entity profiles to filter the stream, and achieved different performance levels, there is no study on and the factors and choices that affect the filtering step itself. Of course filtering has been extensively examined in TREC Filtering \cite{robertson2002trec}. However, those studies were isolated in the sense that they were intended to optimize recall. What we have here is a different scenario. Documents have relevance rating. Thus we want to study filtering in connection to  relevance to the entities and thus can be done by coupling filtering to the later stages of the pipeline. This is new to the best of our knowledge and the TREC KBA problem setting and data-sets offer a good opportunity to examine this aspect of filtering. 

Moreover, there has not been a chance to study at this scale and/or a study into what type of documents defy filtering and why? In this paper, we conduct a manual examination of the documents that are missing and classify them into different categories. We also estimate the general upper bound of recall using the different entities profiles and choose the best profile that results in an increased over all performance as measured by F-measure. 

  \begin{table*}

\caption{Breakdown of sources and delta }

\begin{center}\begin{tabular}{l*{9}{c}r}

 && \multicolumn{3}{ c| }{All entities}  & \multicolumn{3}{ c| }{Wikipedia} &\multicolumn{3}{ c| }{Twitter} \\ 

 & &Others&news&social & Others&news&social &  Others&news&social \\

\hline

\multirow{4}{*}{Vital} &cano                 &82.2 &65.6    &70.9          &90.9 &80.1   &76.8             &8.1   &6.3   &   30.5\\

			 &cano\_part - cano  	&8.2  &14.9    &12.3           &9.1  &18.6   &14.1             &0      &0       &0  \\

                         &all - cano         	&12.6  &19.7    &12.3          &5.5  &15.8   &8.4             &73   &35.9    &38.3  \\

	                 &all\_part - cano\_part&9.7    &18.7  &12.7       &0    &0.5  &5.1        &93.2 & 93 &64.4 \\

	                 &all\_part - all     	&5.4  &13.9     &12.7           &3.6  &3.3    &10.8              &20.3   &57.1    &26.1 \\

	                 \hline

\multirow{4}{*}{Relevant} &cano                 &84.2 &53.4    &55.6          &88.4 &75.6   &63.2             &10.6   &2.2    &  6\\

			 &cano\_part - cano  	&10.5  &15.1    &12.2          &11.1  &21.7   &14.1             &0   &0    &0  \\

                         &all - cano         	&11.7  &36.6    &17.3          &9.2  &19.5   &9.9             &54.5   &76.3   &66  \\

	                 &all\_part - cano\_part &4.2  &26.9   &15.8          &0.2    &0.7    &6.7           &72.2   &87.6 &75 \\

	                 &all\_part - all     	&3    &5.4     &10.7           &2.1  &2.9    &11              &18.2   &11.3    &9 \\

	                 \hline

\multirow{4}{*}{total} 	&cano                &    81.1   &56.5   &58.2         &87.7 &76.3   &65.6          &9.8  &1.4    &13.5\\

			&cano\_part - cano   	&10.9   &15.5   &12.4         &11.9  &21.3   &14.4          &0     &0       &0\\

			&all - cano         	&13.8   &30.6   &16.9         &9.1  &18.9   &10.2          &63.6  &61.8    &57.5 \\

                        &all\_part - cano\_part	&7.2   &24.8   &15.9          &0.1    &0.7    &6.8           &82.2  &89.1    &71.3\\

                        &all\_part - all     	&4.3   &9.7    &11.4           &3.0  &3.1   &11.0          &18.9  &27.3    &13.8\\	                 

\hline

\end{tabular}

\end{center}

\label{tab:source-delta2}

\end{table*}

\multirow{4}{*}{total} 	&cano    &   81.1& 56.5& 58.2& 87.7& 76.4& 65.7& 9.8& 3.6& 13.5\\

&cano part &92.0& 72.0& 70.6& 99.6& 97.7& 80.1& 9.8& 3.6& 13.5\\

&all & 94.8& 87.1& 75.2& 96.8& 95.3& 75.8& 73.5& 65.4& 71.1\\

&all part & 99.2& 96.8& 86.6& 99.8& 98.4& 86.8& 92.4& 92.7& 84.9\\

The results of the breakdown by document categopries is presented in a multi-dimensional table shown in \ref{tab:source-delta}. There are three outer columns for  all entities, Wikipedia and Twitter. Each of the outer columns consist of the document categories of other,news and social. The rows consist of Vital, relevant and total each of which have the four entity profiles.   

 When comparing the recall performances in vital and relevant, we observe that canonical names achieve better in vital than in relevant. This is specially true with Wikipedia entities. For example, the recall for news is 80.1 and for social is 76, while the corresponding recall in relevant is 75.6 and 63.2 respectively. We can generally see that the recall in vital are better than the recall in relevant suggesting that relevant documents are more probable to mention the entities and when they do, using some of their common name variants. 

%  \subsection{Difference by document categories}

%  

% If we look in recall performances, In Wikipedia entities, the order seems to be others, news and social. This means that others achieve a higher recall than news than social.  However, in Twitter entities, it does not show such a strict pattern. In all, entities also, we also see almost the same pattern of other, news and social. 

The recall for Wikipedia entities in \ref{tab:name} ranged from 61.8\% (cannonical names) to 77.9\% (partial names of name variants. We looked at how these recall is distributted across the three document categories. In Table \ref{tab:source-delta}, Wikipedia column, we see, across all entity profiles, that others have a higher recall followed by news. Social documents achieve the lowest recall.  While the news recall  ranged from 76.4\% to 98.4\%, the recall for social documents ranged from 65.7\% to 86.8\%. Others achieve higher than news and news achieve higher than social. This pattern  holds across  all name variants in  Wikipedia  entities. Notice that the others category stands for arxiv (scientific documents), classifieds, forums and linking.

In Twitter entities, however, the pattern is different. In cannonical names (and their partials), social documents achieve higher recall than news . This suggests that social documents refer to Twitter entities by their cannonical names (user names) more than news. In partial names of all name variants, news achieve better results than social. The difference in recall between cannonical and partial names of all name variants shows that news do not refer to Twitter entties by their user names, they refer to them with their display names.

Overall, across all entities types and all entity profiles, others achieve better recall than news, and  news, in turn, achieve higher recall than social documents. This suggests that social documents are the hardest  to retrieve.  This of course makes sense since social posts are short and are more likely to point to other resources, or use short informal names.

We computed four percentage increases in recall (deltas)  between the difefrent entity profiles (see \ref{tab:source-delta2}. The first delta is the recall percentage between partial names of canonical names and canonical names. The second  is the delta between name variants and canonical names. The third is the difference between partial names of name variants  and partial names of canonical names and the fourth between partial names of name variants and name variants. we believe these four deltas offer a clear meaning. The delta between all name variants and canonical names shows the percentage of documents that the new name variants retrieve, but the canonical name does not. Similarly, the delta between partial names of name variants and partial names of canonical names shows the percentage of document-entity pairs that can be gained by the partial names of the name variants. 

In most of the  deltas, news followed by social followed by others show greater difference. This suggests s that news refer to entities by different names, rather than by a certain standard name.  This is counter-intuitive since one would expect news to mention entities by some consistent name(s) thereby reducing the difference. The deltas, for Wikipedia entities, between canonical partials and canonicals,  and all name variants and canonicals are high  suggesting that partial names and all other name variants bring in new docuemnts that can not be retrieved by canonical names. The rest of the two deltas are very small suggesting that partial names of all name variants do not bring in new relevant docuemnts. In Twitter entities,  name variants bring in new documents. 

% The  biggest delta  observed is in Twitter entities between partials of all name variants and partials of canonicals (93\%). delta. Both of them are for news category.  For Wikipedia entities, the highest delta observed is 19.5\% in cano\_part - cano followed by 17.5\% in all\_part in relevant.  

\subsection{Wikipedia versus Twitter}

The tables in \ref{tab:name} and \ref{tab:source-delta} show, recall for Wikipedia entities are higher than for Twitter. This indicates that Wikipedia entities are easier to match in docuemnts than Twitter. This can be due to two reasons: 1) Wikipedia entities are relatively well described rthan Twitter entities. The fact that we can retrieve difeffernt name variants from DBpedia is a measure of relative description. By contrast, we have only two names for Twitter entities:their user names and theur display names wh9ich we collect from their Twitter pages. 2) DbPedia entities are less obscure and that is why they are not in Wikipedia anyways. Another point is that mentioned by their display names more than they are by their user names. We also observed that social docuemnts mention Twitter entities by their user names more than news suggesting a disnction between the standard in news and social documents.