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\documentclass{acm_proc_article-sp}

\usepackage{booktabs}

\usepackage{multirow}

\usepackage{todonotes}

\begin{document}

\title{Entity-Centric Stream Filtering and ranking: Filtering and Unfilterable Documents 

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\maketitle

\begin{abstract}

Entity-centric information processing requires complex pipelines involving both natural language processing and information retrieval components. In entity-centric stream filtering and ranking, the pipeline involves four  important stages: filtering, classification, ranking(scoring)  and evaluation. Filtering is an important step  that creates a manageable working set of documents  from a  web-scale corpus for the next stages.  It thus  determines the performance of the overall system.  Keeping the subsequent steps constant, we  zoom in on the filtering stage and conduct an in-depth analysis of the  main components of cleansing, entity profiles, relevance levels, category of documents and entity types with a view to understanding  the factors and choices that affect filtering performance. The study demonstrates the most  effective entity profiling,  identifies those relevant documents that defy filtering and conducts manual examination into their contents. The paper classifies the ways unfilterable documents 

are mentioned in text and estimates the practical upper-bound of recall in  entity-based filtering.  

\end{abstract}

% A category with the (minimum) three required fields

\category{H.4}{Information Filtering}{Miscellaneous}

%A category including the fourth, optional field follows...

%\category{D.2.8}{Software Engineering}{Metrics}[complexity measures, performance measures]

\terms{Theory}

\keywords{Information Filtering; Cumulative Citation Recommendation; knowledge maintenance; Stream Filtering;  emerging entities} % NOT required for Proceedings

\section{Introduction}

  In 2012, the Text REtrieval Conferences (TREC) introduced the Knowledge Base Acceleration (KBA) track  to help Knowledge Bases(KBs) curators. The track is crucial to address a critical need of KB curators: given KB (Wikipedia or Twitter) entities, filter  a stream  for relevant documents, rank the retrieved documents and recommend them to the KB curators. The track is crucial and timely because  the number of entities in a KB on one hand, and the huge amount of new information content on the Web on the other hand make the task of manual KB maintenance challenging.   TREC KBA's main task, Cumulative Citation Recommendation (CCR), aims at filtering a stream to identify   citation-worthy  documents, rank them,  and recommend them to KB curators.

 Filtering is a crucial step in CCR for selecting a potentially relevant set of working documents for subsequent steps of the pipeline out of a big collection of stream documents. 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}. Adaptive Filtering, one task of the filtering track,  starts with   a persistent user profile and a very small number of positive examples. The  filtering step used in CCR systems fits under adaptive filtering: the profiles are represented by persistent KB (Wikipedia or Twitter) entities and there is a small set of relevance judgments representing positive examples. 

 TREC-KBA 2013's participants applied Filtering as a first step  to produce a smaller working set for subsequent experiments. As the subsequent steps of the pipeline use the output of the filter, the final performance of the system is dependent on this important step.  The filtering step particularly determines the recall of the overall system. However, all submitted systems suffered from poor recall \cite{frank2013stream}.  The most important components of the filtering step are cleansing, and entity profiling. Each component has choices to make. For example, there are two versions of corpus: cleansed and raw. Different approaches used different entity profiles for filtering. These entity profiles varied from  KB entities' canonical names, to  DBpedia name variants, to using bold words in the first paragraph of the Wikipedia entities’ profiles and anchor texts from other Wikipedia pages, to using exact name and wordNet synonyms. Moreover, the Type of entities (Wikipedia or Twitter), the category of 

documents (news, blog, tweets) can influence filtering.

 A variety of approaches are employed  to solve the CCR challenge. Each participant reports the steps of the pipeline and the final results in comparison to other systems.  A typical TREC KBA poster presentation or talk explains the system pipeline and reports the final results. The systems may employ similar (even the same) steps  but the choices they make at every step are usually different. In such a situation, it becomes hard to identify the factors that result in improved performance. There is  a lack of insight across different approaches. This makes  it hard to know  whether the improvement in performance of a particular approach is due to preprocessing, filtering, classification, scoring  or any of the sub-components of the pipeline. 

 In this paper,  we hold the subsequent steps of the pipeline fixed, zoom in on the filtering step and  conduct an in-depth analysis of the main components in it.  In particular, we study  cleansing, different entity profiling,  type of entities (Wikipedia or Twitter), and type of documents (social, news, etc).  The main contribution of the paper: 

 An in-depth analysis of the factors that affect entity-based stream filtering

 Identifying optimal entity profiles vis-avis not compromising precision

 Describing relevant documents that are not amenable to filtering and thereby estimating the upper-bound on entity-based filtering

 The rest of the paper is is organized as follows: 

 \section{Data and Task description}

\begin{table*}

\caption{retrieved documents to different sources }

\begin{center}

 \begin{tabular}{l*{4}{l}l}

 documents     &   chunk files    &    Sub-stream \\

\hline

126,952         &11,851         &arxiv \\

394,381,405      &   688,974        & social \\

134,933,117       &  280,658       &  news \\

5,448,875         &12,946         &linking \\

57,391,714         &164,160      &   MAINSTREAM\_NEWS (spinn3r)\\

36,559,578         &85,769      &   FORUM (spinn3r)\\

14,755,278         &36,272     &    CLASSIFIED (spinn3r)\\

52,412         &9,499         &REVIEW (spinn3r)\\

7,637         &5,168         &MEMETRACKER (spinn3r)\\

1,040,520,595   &      2,222,554 &        Total\\

\end{tabular}

\end{center}

\label{tab:streams}

\end{table*}

\subsection{KB entities}

\subsection{Relevance judgments}

 \subsection{Research questions}

 Given a stream of documents of news items, blogs and social media on one hand and KB entities (Wikipedia, Twitter)  on the other,  we study the cleansing step, the entity-profile construction, the category of the stream items, the type of entities (Wikipedia or Twitter), and the impact on classification.  In particular, we strive to answer the following 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?

  \item Are there vital (relevant) documents that are not filterable by a reasonable system?

\end{enumerate}

\subsection{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 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. 

\section{Method}

We work with the subset of stream corpus documents  for whom there exist  annotation. For this purpose, we extracted the documents that have annotation from the big corpus. All our experiments are based on this smaller subset.   We experiment with all KB entities.  For each KB entity, we extract different name variants from DBpedia and Twitter. 

\

\subsection{Entity Profiling}

We build profiles for the KB entities of interest. We have two types: Twitter and Wikipedia. Both Entities are selected, on purpose, to be sparse, less-documented.  For the Twitter entities, we visit their respective Twitter pages  and  manually fetch their display names from their Twitter pages. For the Wikipedia entities, we fetch different name variants from DBpedia, namely  name, label, birth name, alternative names, redirects, nickname, or alias.  The extraction results are in Table \ref{tab:sources}.

\begin{table*}

\caption{Number of retrieved name variants for different sources }

\begin{center}

 \begin{tabular}{l*{4}{c}l}

 &Name Variant&Number of strings \\

\hline

 Name  &82&82\\

 Label   &121 &121\\

Redirect  &49&96 \\

 Birth Name &6&6\\

 Nickname & 1&1&\\

 Alias &1 &1\\

 Alternative Names &4&4\\

\hline

\end{tabular}

\end{center}

\label{tab:sources}

\end{table*}

We have a total of 121 Wikipedia entities. Not every entity has a value for the different name variants. Every entity has a DBpedia label.  Only 82 entities have a name string and only 49 entities have redirect strings. Most of the entities have only one string, but some have 2, 3, 4 and 5. One entity, Buddy\_MacKay,has the highest (12) number of redirect strings. 6 entities have  birth names, only 1 ( Chuck Pankow) has a nick name,  ``The Peacemaker'',  only 1 entity has alias and only 4 have alternative names.

We combined the different name variants we extracted to form a set of strings for each KB entity. Specifically, we merged the names, labels, redirects, birth names, nick names and alternative names of each entity. For Twitter entities, we used the display names that we collected. We consider the names of that entities that are part of the URL as canonical names . For example in http://en.wikipedia.org/wiki/Benjamin\_Bronfman, Benjamin Bronfman is a canonical name of the entity. From the combined name variants and the canonical names, we  created four sets of profiles for each entity:  canonical names (cano), partial names of canonical names (cano\_part), all names variants  (all) and partial names of all names(all\_part). 

\subsection{Annotation Corpus}

The annotation set is a combination of the annotations from before the Training Time Range(TTR) and Evaluation Time Range (ETR) and consists of 68405 annotations.  Its breakdown into training and test sets is  shown in Table \ref{tab:breakdown}.

\begin{table}

\caption{Number of annotation documents with respect to different categories,(cleansed, raw, training, testing}

\begin{center}

\begin{tabular}{l*{3}{c}r}

 &&Vital&Relevant  &Total \\

\hline

\multirow{2}{*}{Training}  &Wikipedia & 1932  &2051& 3672\\

			  &Twitter&189   &314&488 \\

			   &All Entities&2121&2365&4160\\

\hline 

\multirow{2}{*}{Testing}&Wikipedia &6139   &12375 &16160 \\

                         &Twitter&1261   &2684&3842  \\

                         &All Entities&7400   &12059&20002 \\

             \hline 

\multirow{2}{*}{Total} & Wikipedia       &8071   &14426&19832  \\

                       &Twitter  &1450  &2998&4330  \\

                       &All entities&9521   &17424&24162 \\

\hline

\end{tabular}

\end{center}

\label{tab:breakdown}

\end{table}

Most (more than 80\%) of the annotation documents are in the test set. Some annotation documents do not have a cleansed content. In both the training and test data for 2013, there are  68405 annotations, of which 50688 are unique document-entity pairs.   Out of 50688,  24162  unique document-entity pairs vital or relevant, of which 9521 are vital and 17424 are relevant. 

\section{Experiments and Results}

 We conducted experiments to study  the effect of cleansing, different entity profiles, types of entities, category of documents, relevance ranks (vital or relevant), and the impact on classification.  For ease of understanding, we present the results in two categories: cleansing, and document categories. In each case we study the number of annotated documents that are retrieved. 

\begin{table}

\caption{Central or relevant documents that are retrieved under different name variants , upper part from cleansed, lower part from raw}

\begin{center}

\begin{tabular}{l@{\quad}lllllll}

\hline

\multicolumn{1}{l}{\rule{0pt}{12pt}}&\multicolumn{1}{l}{\rule{0pt}{12pt}cano}&\multicolumn{1}{l}{\rule{0pt}{12pt}cano partial }&\multicolumn{1}{l}{\rule{0pt}{12pt}all }&\multicolumn{1}{l}{\rule{0pt}{50pt}all\_part}\\[5pt]

\hline

   all-entities   &51.0  &61.7  &66.2  &78.4 \\	

   Wikipedia      &61.8  &74.8  &71.5  &77.9\\

   twitter        &1.9   &1.9   &41.7  &80.4\\

\hline

\hline

  all-entities    &59.0  &72.2  &79.8  &90.2\\

   Wikipedia      &70.0  &86.1  &82.4  &90.7\\

   twitter        & 8.7  &8.7   &67.9  &88.2\\

\hline

\end{tabular}

\end{center}

\label{tab:name}

\end{table}

The upper part of Table \ref{tab:name} shows the recall performances on the cleansed version and the lower part on the raw version. The recall performances for all entity types  are increased substantially in the raw version. Recall increases on Wikipedia entities  vary from 8.2 to 12.8, and in Twitter entities from 6.8 to 26.2. In all entities, it varies from 8.0 to 13.6.  The recall increases are substantial. To put it into perspective, an 11.8 increase in recall on all entities is a retrieval of 2864 more unique document-entity pairs. This suggests that cleansing has removed some documents that we could otherwise retrieve. 

\subsection{Entity Profiles}

If we look at the recall performances for the raw corpus,   filtering documents by canonical names achieves a recall of  59\%.  Adding other name variants  improves the recall to 79.8\%, an increase of 20.8\%. This means  20.8\% of documents mentioned the entities by other names  rather than by their canonical names. Canonical partial names achieve a recall of 72\%  and the partial names of all name variants achieves 90.2\%. This says that 18.2\% of documents mentioned the entities by  partial names of other non-canonical name variants. 

\subsection{Breakdown of results by document source category}

  \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*}

 \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 & 90.4& 80.6& 83.1& 100.0& 98.7& 90.9&   8.1&  6.3&  30.5\\

&all  & 94.8& 85.4& 83.1& 96.4&  95.9& 85.2&   81.1& 42.2& 68.8\\

&all part &100& 99.2& 95.9& 100.0&  99.2& 96.0&   100&  99.3& 94.9\\

\hline

\multirow{4}{*}{Relevant} &cano & 84.2& 53.4& 55.6& 88.4& 75.6& 63.2& 10.6& 2.2& 6.0\\

&cano part &94.7& 68.5& 67.8& 99.6& 97.3& 77.3& 10.6& 2.2& 6.0\\

&all & 95.8& 90.1& 72.9& 97.6& 95.1& 73.1& 65.2& 78.4& 72.0\\

&all part &98.8& 95.5& 83.7& 99.7& 98.0& 84.1& 83.3& 89.7& 81.0\\

	                 \hline

\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\\

\hline

\end{tabular}

\end{center}

\label{tab:source-delta}

\end{table*}

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). 

The 8 document source categories are regrouped into three 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. 

 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}

%  

%  Generally, there is greater variation in relevant rank than in vital. This is specially true in most of the Delta's for Wikipedia. This  maybe be explained by news items referring to  vital documents by a some standard name than documents that are relevant. Twitter entities show greater deltas than Wikipedia entities in both vital and relevant. The greater variation can be explained by the fact that the canonical name of Twitter entities retrieves very few documents. The deltas that involve canonical names of Twitter entities, thus, show greater deltas.  

%  

% 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. 

\subsection{Document category: others. news and social}

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.

% 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{Impact on classification}

  In the overall experimental setup, the goal is to keep the classification constant. In here, we present results showing how filtering affects performance.  In tables \ref{tab:class-vital} and \ref{tab:class-vital-relevant}, we show the performances in F-measure and SU. 

\begin{table*}

\caption{vital performance under different name variants , upper part from cleansed, lower part from raw}

\begin{center}

\begin{tabular}{ll@{\quad}lllllll}

\hline

\multicolumn{1}{l}{\rule{0pt}{12pt}}&\multicolumn{1}{l}{\rule{0pt}{12pt}cano}&\multicolumn{1}{l}{\rule{0pt}{12pt}cano partial }&\multicolumn{1}{l}{\rule{0pt}{12pt}all }&\multicolumn{1}{l}{\rule{0pt}{50pt}all\_part}\\[5pt]

\hline{Cleansed}

   all-entities &F& 0.241&0.261&0.259&0.265\\

	      &SU&0.259  &0.258 &0.263 &0.262 \\	

   Wikipedia &F&0.252&0.274& 0.265&0.271\\

	      &SU& 0.261& 0.259&  0.265&0.264 \\

   twitter &F&0.105&0.105&0.218&0.228\\

     &SU &0.105&0.250& 0.254&80.253\\

\hline

\hline

  all-entities &F & 0.240 &0.272 &0.250&0.251\\

	  &SU& 0.258   &0.151  &0.264  &0.258\\

   Wikipedia&F &0.257&0.257&0.257&0.255\\

   &SU	     & 0.265&0.265 &0.266 & 0.259\\

   twitter&F &0.188&0.188&0.208&0.231\\

	&SU&    0.269 &0.250\% &0.250&0.253\\

\hline

\end{tabular}

\end{center}

\label{tab:class-vital}

\end{table*}

  \begin{table*}

\caption{vital or relevant performances under different name variants , upper part from cleansed, lower part from raw}

\begin{center}

\begin{tabular}{ll@{\quad}lllllll}

\hline

\multicolumn{1}{l}{\rule{0pt}{12pt}}&\multicolumn{1}{l}{\rule{0pt}{12pt}cano}&\multicolumn{1}{l}{\rule{0pt}{12pt}cano partial }&\multicolumn{1}{l}{\rule{0pt}{12pt}all }&\multicolumn{1}{l}{\rule{0pt}{50pt}all\_part}\\[5pt]

\hline{Cleansed}

   all-entities &F& 0.497&0.560&0.579&0.607\\

	      &SU&0.468  &0.484 &0.483 &0.492 \\	

   Wikipedia &F&0.546&0.618&0.599&0.617\\

   &SU&0.494  &0.513 &0.498 &0.508 \\

   twitter &F&0.142&0.142& 0.458&0.542\\

    &SU &0.317&0.328&0.392&0.392\\

\hline

\hline

  all-entities &F& 0.509 &0.594 &0.590&0.612\\

    &SU       &0.459   &0.502  &0.478  &0.488\\

   Wikipedia &F&0.550&0.617&0.605&0.618\\

   &SU	     & 0.483&0.498 &0.487 & 0.495\\

   twitter &F&0.210&0.210&0.499&0.580\\

	&SU&    0.319  &0.317 &0.421&0.446\\

\hline

\end{tabular}

\end{center}

\label{tab:class-vital-relevant}

\end{table*}

We have looked into the effect of cleansing in filtering. Further, we have looked into the retrieval effectiveness of different profiles of entities.  We also looked into weather the source categories have an effect on filtering. 

Let's look into cleansed versus raw on  Wikipedia entities: It seems that the cleansed version gives better results than the raw version in three of the profiles(except in canonical). However, in Twitter entities, the raw corpus achieves a better score in all profiles (except in name variants profiles).  In all entities, we see that in three profiles, cleansed achieves better (only in canonical partial, does raw perform better.) 

In terms of profiles, Wikipedia's canonical partial names seem to achieve the highest performance. For Twitter, the partial names of name variants achieve  better results. 

For vital plus relevant:

In three cases, raw achieves better results (except in cano partials). For Twitter entities, the raw corpus achieves better results.  In terms of  entity profiles, Wikipedia's canonical partial names achieve  the best F-score. For Twitter, as before, partial names of canonical names. 

It seems, the raw corpus has more effect on Twitter entities performances. An increase in recall does not necessarily mean an increase in F-measure. 

\subsection{Missing relevant documents \label{miss}}

\begin{table}

\caption{The number of documents that are missing from raw and cleansed extractions. }

\begin{center}

\begin{tabular}{l@{\quad}llllll}

\hline

\multicolumn{1}{l}{\rule{0pt}{12pt}category}&\multicolumn{1}{l}{\rule{0pt}{12pt}Vital }&\multicolumn{1}{l}{\rule{0pt}{12pt}Relevant }&\multicolumn{1}{l}{\rule{0pt}{12pt}Total }\\[5pt]

\hline

Cleansed &1284 & 1079 & 2363 \\

Raw & 276 & 4951 & 5227 \\

\hline

 missing only from cleansed &1065&2016&3081\\

  missing only from raw  &57 &160 &217 \\

  Missing from both &219 &1927&2146\\

\hline

\end{tabular}

\end{center}

\label{tab:miss}

\end{table}

converting.  In both cases the mention of the entity happened to be on the part of the text that is cut out during conversion. 

%    

%    \begin{table*}

% \caption{Breakdown of missing documents by sources for cleansed, raw and cleansed-and-raw}

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

%   &others&news&social \\

% \hline

% 

% 			&missing from raw only &   0 &0   &217 \\

% 			&missing from cleansed only   &430   &1321     &1341 \\

% 

%                          &missing from both    &19 &317     &2196 \\

%                         

%                          

% 

% \hline

% \end{tabular}

% \end{center}

% \label{tab:miss-category}

% \end{table*}

\paragraph{Politician - constituency} A major political event in a certain constituency is vital for the politician from that constituency. 

 A good example is a weblog that talks about two north Dakota counties being drought disasters. The news is vital for Joshua Boschee, a politician, a member of North Dakota democratic party.  

\paragraph{World Knowledge} Some things are impossible to know without your world knowledge. For example ''refreshments, treats, gift shop specials, "bountiful, fresh and fabulous holiday decor," a demonstration of simple ways to create unique holiday arrangements for any home; free and open to the public`` is judged relevant to Hjemkomst\_Center. This is a social media post, and unless one knows the person posting it, there is no way that this text shows that. Similarly ''learn about the gray wolf's hunting and feeding behaviors and watch the wolves have their evening meal of a full deer carcass; $15 for members, $20 for nonmembers`` is judged vital to Red\_River\_Zoo.  

\paragraph{No document content} Some documents were found to have no content

\paragraph{Not clear why} It is not clear why some documents are annotated vital for some entities.

%    To gain more insight, I sampled for each 35 entities, one document-entity pair and looked into the contents. The results are in \ref{tab:miss from both}

%    

%    \begin{table*}

% \caption{Missing documents and their mentions }

% \begin{center}

% 

%  \begin{tabular}{l*{4}{l}l}

%  &entity&mentioned by &remark \\

% \hline

%  Jeremy McKinnon  & Jeremy McKinnon& social, mentioned in read more link\\

% Blair Thoreson   & & social, There is no mention by name, the article talks about a subject that is political (credit rating), not apparent to me\\

%   Lewis and Clark Landing&&Normally, maha music festival does not mention ,but it was held there \\

% Cementos Lima &&It appears a mistake to label it vital. the article talks about insurance and centos lima is a cement company.entity-deleted from wiki\\

% Corn Belt Power Cooperative & &No content at all\\

% Marion Technical Institute&&the text could be of any place. talks about a place whose name is not mentioned. 

%  roryscovel & &Talks about a video hinting that he might have seen in the venue\\

% Jim Poolman && talks of party convention, of which he is member  politician\\

% Atacocha && No mention by name The article talks about waste from mining and Anacocha is a mining company.\\

% Joey Mantia & & a mention of a another speeedskater\\

% Derrick Alston&&Text swedish, no mention.\\

% Paul Johnsgard&& not immediately clear why \\

% GandBcoffee&& not immediately visible why\\

% Bob Bert && talks about a related media and entertainment\\

% FrankandOak&& an article that talks about a the realease of the most innovative companies of which FrankandOak is one. \\

% KentGuinn4Mayor && a theft in a constituency where KentGuinn4Mayor is vying.\\

% Hjemkomst Center && event announcement without mentioning where. it takes a a knowledge of \\

% BlossomCoffee && No content\\

% Scotiabank Per\%25C3\%25BA && no content\\

% Drew Wrigley && politics and talk of oilof his state\\

% Joshua Zetumer && mentioned by his film\\

% Théo Mercier && No content\\

% Fargo Air Museum && No idea why\\

% Stevens Cooperative School && no content\\

% Joshua Boschee && No content\\

% Paul Marquart &&  No idea why\\

% Haven Denney && article on skating competition\\

% Red River Zoo && animal show in the zoo, not indicated by name\\

% RonFunches && talsk about commedy, but not clear whyit is central\\

% DeAnne Smith && No mention, talks related and there are links\\

% Richard Edlund && talks an ward ceemony in his field \\

% Jennifer Baumgardner && no idea why\\

% Jeff Tamarkin && not clear why\\

% Jasper Schneider &&no mention, talks about rural development of which he is a director \\

% urbren00 && No content\\

% \hline

% \end{tabular}

% \end{center}

% \label{tab:miss from both}

% \end{table*}

\section{Analysis and Discussion}

We conducted experiments to study  the impacts on recall of 

different components of the filtering step of the CCR pipeline. Specifically 

we conducted experiments to study the impacts of cleansing, 

entity profile, relevance rankings, categories of documents, and documents that are missed.

Experimental results using TREC-KBA task show that cleansing removes documents  or part of the documents making them difficult to retrieve. These documents can, otherwise, be retrieved from the raw version. The use of the raw corpus brings in documents that can not be retrieved from the cleansed corpus. This is true for all entity profiles and for all entity types. The  recall increase is  between 6.8\% to 26.2\%. These increase, in actual document-entity pairs,  is in thousands. 

The use of different profiles also shows a big difference in percentage recall. Except in the case of Wikipedia, where the use of canonical\_partial achieves better than name variants, there is a steady increase in recall from canonical names to partial canonical names, name variants and partial names of name variants. The difference between partial names of name variants and canonical names is 30.8\%. And between partial names of name variants and partial names of canonical names is 18.0\%.  

Does this increase in recall as move from a less richer profile to a more richer profile translate to an increase in classification performance? The results show that it does not. In most profiles, for both Wikipedia and total entities, the cleansed version performs better than the raw version. In Twitter entities, the raw corpus achieves better except in the case of all name variants. However, the difference in performance are so small that the increase can be ignored. The highest performance for Wikipedia entities is achieved with partial names of canonical names, rather than partial names of all names variants which retrieve 18.0\% more documents. 

However, for vital plus relevant, the raw corpus performs  better except in partial canonical names. In all cases, Wikipedia's canonical partial names achieves better performance than any other profile. This is interesting because the retrieval of thousands of document-entity pairs did not translate to an increase in performance  in classification. 

One reason why an increase in recall does not translate to an increase in F-measure later is because of the retrieval of many false positives which confuse the classifier. A good profile for Wikipedia entities seem canonical partial names suggesting that there is actually no need to go and fetch different names variants.

For Twitter entities, the use of partial names of their display names are a good entity profiles. 

\section{Conclusions}

In this paper, we examined the filtering step of the CCR pipeline by holding the classification stage constant. In particular, we studied the cleansing step, different entity profiles, type of entities (Wikipedia or Twitter), categories of documents (news, social, or others) and the vital (relevant) documents. While doing so, we attempted to find answers to these 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}

While the use of the raw corpus and some entity profiles increases recall substantially, it did not bring a considerable improvement on classification performance. While the absence of  increase in performance for some entity profiles can be explained by the fact that they bring in false positives that confuse the system, the absence of considerable increase in performance in the raw corpus defies explanation. 

Although both Wikipedia and Twitter entities are similar in the sense that they both are sparse entities, our results warrant that they be treated differently. We have seen, for example, that Wikipedia entities best profile representation is the use of partial names of canonical names while Twitter Entities' best profile is the partial names of name variants. Moreover, we have seen that Twitter entities achieve better results with the raw corpus while Wikipedia entities with the cleansed corpus. 

The categories of documents also have an impact on performance. News items show greater variation in performance between the different Entity profiles. This indicates that news items use a wide, non-uniform way of referring entities more than social documents. This is counter-intuitive for one would normally expect news to be more standardized than social (blogs and tweets).

As we strive to use different entity profiles from less rich to very rich, we observe that there are still documents that we still miss no matter what. While some are possible to retrieve with some modifications, some others are not. There are some document that indicate that computers do not seem to get them no matter how rich representation of entities they use. These are documents such as those that  are mentioned in one of the following ways: in a page linked to by 

read more,

venue-event, world knowledge, party-politician, company-related event, entity-related entity, artist - artists work. We also found that some documents have no content at all. Another group of documents ones for which it is not clear why they are relevant. 

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