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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 stages: filtering, classification,

ranking (scoring) and evaluation. Filtering is an initial step, that

extracts a working-set of documents from the web-scale corpus, aiming

for a smaller size collection that would be more manageable in the

subsequent stages of the pipeline. This filtering step therefore

determines the maximally attainable performance of the overall system.

This paper investigates the filtering stage in isoltation, in context

of a cumulative citation recommendation problem. We conduct an

in-depth analysis of the main factors that determine filtering

effectiveness: cleansing noisy web data, methods to create entity

profiles, the types of entity of interest, document category, and the

relevance level of the entity-document pair under consideration.

We analyze how these factors (and the design choices made in their

corresponding system components) affect filtering performance.

We identify and characterize the relevant documents that do not pass the

filtering stage, and conduct a 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 document categories (social, news, etc).  The main contribution of the paper are an in-depth analysis of the factors that affect entity-based stream filtering, identifying optimal entity profiles vis-avis not compromising precision, describing and classifying relevant documents that are not amenable to filtering , and  estimating the upper-bound of recall on entity-based filtering.

 The rest of the paper is is organized as follows: 

 \section{Data Description}

We use TREC-KBA 2013 dataset \footnote{http://http://trec-kba.org/trec-kba-2013.shtml}. The dataset consists of a time-stamped  stream corpus, a set of KB entities, and a set of relevance judgments. 

\subsection{Stream corpus} The stream corpus comes in two versions: raw and cleaned. The raw  and cleansed versions are 6.45TB and 4.5TB respectively,  after xz-compression and GPG encryption. The raw data is a  dump of  raw HTML pages. The cleansed version is the raw data after its HTML tags are stripped off and only English documents identified with Chromium Compact Language Detector \footnote{https://code.google.com/p/chromium-compact-language-detector/} are included.  The stream corpus is organized in hourly folders each of which contains many  chunk files. Each chunk file contains between hundreds and hundreds of thousands of serialized  thrift objects. One thrift object is one document. A document could be a blog article, a news article, or a social media post (including tweet).  The stream corpus comes from three sources: TREC KBA 2012 (social, news and linking) \footnote{http://trec-kba.org/kba-stream-corpus-2013.shtml}, arxiv\footnote{http://arxiv.org/}, and spinn3r\footnote{http://spinn3r.com/}. Table \ref{tab:streams}   shows the sources, the number of hourly directories, and the number of chunk files. 

\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}

 The KB entities consist of 20 Twitter entities and 121 Wikipedia entities. The selected entities are, on purpose, sparse. The entities consist of 71 people, 1 organization, and 24 facilities.  

\subsection{Relevance judgments}

TREC-KBA provided relevance judgments for training and testing. Relevance judgments are given to a document-entity pairs. Documents with citation-worthy content to a given entity are annotated  as \emph{vital},  while documents with tangentially relevant content, or documents that lack freshliness o  with content that can be useful for initial KB-dossier are annotated as \emph{relevant}. Documents with no relevant content are labeled \emph{neutral} and spam is labeled as \emph{garbage}.  The inter-annotator agreement on vital in 2012 was 70\% while in 2013 it is 76\%. This is due to the more refined definition of vital and the distinction made between vital and relevant. 

 \subsection{Stream Filtering}

 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 factors and choices that affect filtering performance. Specifically, we conduct in-depth analysis on the cleansing step, the entity-profile construction, the document category of the stream items, and the type of entities (Wikipedia or Twitter). We also study the impact of choices on classification performance. Finally, we conduct manual examination of the relevant documents that defy filtering. We strive to answer the following 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}

\subsection{Evaluation}

The TREC filtering track 

\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 is 1.9TB after xz-compression and has  400M documents. By contrast, the KBA 2013 corpus is 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, a change in the definitions of relevance ratings vital and relevant. While in KBA 2012, a document was judged vital if it has citation-worthy content for a given entity, in 2013 it must have the freshliness, that is the content must trigger an editing of the given entity's KB entry. 

While the tasks of 2012 and 2013 are fundamentally the same, the approaches  varied due  to the size of the corpus. In 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 most of the participants used DBpedia name variants none of them used all the name variants.  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}. One participant used Boolean \emph{and} built from the tokens of the canonical names \cite{illiotrec2013}.  

All of the studies 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. 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 different DBpedia name variants}

\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.  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 several redirect sterings. One entity, Buddy\_MacKay, has the highest (12) number of redirect strings. 6 entities have  birth names, 1 entity has a nick name, 1 entity has alias and  4 entities have alternative names.

We combined the different name  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 the entities that are part of the URL as canonical. 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 (cao) canonical partial (cano-part), all name variants combined (all) and partial names of all name variants(all-part). the names in paranthesis are used in table captions.

\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(relevance rating, training and 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.  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 are vital-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.  In the following subsections, we present the results in different categories, and describe them.

 \subsection{Cleansing: raw or cleansed}

\begin{table}

\caption{vital-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

&cano&cano-part  &all &all-part  \\

\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 ranges 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  achieves a recall of 72\%  and name-variant partial achives 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  recall percentage increases by document categories }

\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-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-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-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 recall performances by document source category}

\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 results of the break down are presented in Table \ref{tab:source-delta}.  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. 

 \subsection{ Relevance Rating: vital and relevant}

 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{Recall across document categories: others, news and social}

The recall for Wikipedia entities in Table \ref{tab:name} ranged from 61.8\% (canonicals) to 77.9\% (name-variants).  Table \ref{tab:source-delta} shows how recall is distributed across document categories. For Wikipedia entities, across all entity profiles, others have a higher recall followed by news, and then by social.  While the recall for news  ranged from 76.4\% to 98.4\%, the recall for social documents ranged from 65.7\% to 86.8\%. In Twitter entities, however, the pattern is different. In canonicals (and their partials), social documents achieve higher recall than news. 

%This indicates that social documents refer to Twitter entities by their canonical names (user names) more than news do. In name- variant partial, news achieve better results than social. The difference in recall between canonicals and name-variants show that news do not refer to Twitter entities by their user names, they refer to them by their display names.

Overall, across all entities types and all entity profiles, others achieve higher recall than news, and  news, in turn, achieve higher recall than social documents. 

% This suggests that social documents are the hardest  to retrieve.  This  makes sense since social posts such as tweets and blogs 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 different entity profiles (Table \ref{tab:source-delta2}). The first delta is the recall percentage between canonical partial  and canonical. The second  is  between name= variant and canonical. The third is the difference between name-variant partial  and canonical partial and the fourth between name-variant partial and name-variant. we believe these four deltas offer a clear meaning. The delta between name-variant and canonical measn the percentage of documents that the new name variants retrieve, but the canonical name does not. Similarly, the delta between  name-variant partial and partial canonical-partial means the percentage of document-entity pairs that can be gained by the partial names of the name variants. 

% 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{Entity Types: Wikipedia and Twitter)}

Table \ref{tab:name} shows the difference between Wikipedia and Twitter entities.  Wikipedia entities' canonical achieves a recall of 70\%, and canonical partial  achieves a recall of 86.1\%. This is an increase in recall of 16.1\%. By contrast, the increase in recall of name-variant partial over name-variant is 8.3.   

%The high increase in recall when moving from canonical names  to their partial names, in comparison to the lower increase when moving from all name variants to their partial names can be explained by saturation. This is to mean that documents have already been extracted by the different name variants and thus using their partial names does not bring in many new relevant documents. 

One interesting observation is that, For Wikipedia entities, canonical partial achieves better recall than name-variant in both cleansed and raw corpus.  %In the raw extraction, the difference is about 3.7. 

In Twitter entities, however, it is different. Both canonical and their partials perform the same and the recall is very low. Canonical  and canonical partial are the same for Twitter entities because they are one word strings. For example in https://twitter.com/roryscovel, ``roryscovel`` is the canonical name and its partial is also the same.  

%The low recall is because the canonical names of Twitter entities are not really names; they are usually arbitrarily created user names. It shows that  documents  refer to them by their display names, rarely by their user name, which is reflected in the name-variant recall (67.9\%). The use of name-variant partial increases the recall to 88.2\%.

The tables in \ref{tab:name} and \ref{tab:source-delta} show recall for Wikipedia entities are higher than for Twitter. Generally, at both aggregate and document category levels, we observe that recall increases as we move from canonicals to canonical partial, to name-variant, and to name-variant partial. The only case where this does not hold is in the transition from Wikipedia's canonical partial to name-variant. At the aggregate level(as can be inferred from Table \ref{tab:name}), the difference in performance between  canonical  and name-variant partial is 31.9\% on all entities, 20.7\% on Wikipedia entities, and 79.5\% on Twitter entities. This is a significant performance difference. 

   \subsection{Impact on classification}

%   In the overall experimental setup, classification, ranking,  and evaluationn are kept constant. 

  In here, we present results showing how  the choices in corpus, entity types, and entity profiles impact these latest stages of the pipeline.  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}canonical partial }&\multicolumn{1}{l}{\rule{0pt}{12pt}name-variant }&\multicolumn{1}{l}{\rule{0pt}{50pt}name-variant partial}\\[5pt]

  &&cano&cano-part&all  &all-part \\

   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-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}canonical}&\multicolumn{1}{l}{\rule{0pt}{12pt}canonical partial }&\multicolumn{1}{l}{\rule{0pt}{12pt}name-variant }&\multicolumn{1}{l}{\rule{0pt}{50pt}name-variant partial}\\[5pt]

 &&cano&cano-part&all  &all-part \\

   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*}

Table \ref{tab:class-vital} shows the recall performance for vitally judged documents.  On Wikipedia entities, except in the canonical profile, the cleansed version achieves  better results than the raw version.  However, on Twitter entities, the raw corpus achieves  better  in all entity profiles (except  in name-variant partial).  At an aggregate (both Wikipedia and Twitter) level, we see that in three profiles, cleansed achieves better.  Only in canonical partial, does raw perform better. Overall cleansed achieves better results than raw.  This result is interesting because we saw in previous sections that the raw corpus achieves  higher recall than cleansed. In the case name-variant partial, for example, 10\% more relevant documents are retrieved in the raw corpus. The gain in recall in raw corpus does not translate into a gain in F\_measure. In fact, in most cases F\_measure decreased. % One explanation for this is that it brings in many false positives from, among related links, adverts, etc.  

For Wikipedia entities,  canonical partial  achieves the highest performance. For Twitter, name-variant partial achieves  better results.

In vital-relevant category (Table \ref{tab:class-vital-relevant}), the performances are different.  Except in canonical partial,  raw achieves better results in all cases. For Twitter entities, the raw corpus achieves better results in all cases.  In terms of  entity profiles, Wikipedia's canonical partial  achieves  the best F-score. For Twitter, as before, canonical partial. The raw corpus has more effect on relevant documents and Twitter entities.  

%The fact that canonical partial names achieve better results is interesting.  We know that partial names were used as a baseline in TREC KBA 2012, but no one of the KBA participants actually used partial names for filtering.

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

T

% 

 The use of name-variant partial for filtering is an aggressive attempt to retrieve as many relevant documents as possible at the cost of retrieving irrelevant documents. However, we still miss about  2363(10\%) of the vital-relevant documents.  Why are these documents missed? If they are not mentioned by partial names of name variants, what are they mentioned by? Table \ref{tab:miss} shows the documents that we miss with respect to cleansed and raw corpus.  The upper part shows the number of documents missing from cleansed and raw versions of the corpus. The lower part of the table shows the intersections and exclusions in each corpus.  

\begin{table}

\caption{The number of documents 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}

One would  assume that  the set of document-entity pairs extracted from cleansed are a sub-set of those   that are extracted from the raw corpus. We find that that is not the case. There are 217  unique entity-document pairs that are retrieved from the cleansed corpus, but not from the raw. 57 of them are vital.    Similarly,  there are  3081 document-entity pairs that are missing  from cleansed, but are present in  raw. 1065 of them are vital.  Examining the content of the documents reveals that it is due to a missing part of text from a corresponding document.  All the documents that we miss from the raw corpus are social. These are documents such as tweets and blogs, posts from other social media. To meet the format of the raw data (binary byte array), some of them must have been converted later, after collection and on the way lost a part or the entire content. It is similar for the documents that we miss from cleansed: a part or the entire content  is lost in during the cleansing process (the removal of HTML tags and non-English documents).  In both cases the mention of the entity happened to be on the part of the text that is cut out during transformation. 

 The interesting set  of relevance judgments are those that  we miss from both raw and cleansed extractions. These are 2146 unique document-entity pairs, 219 of them are with vital relevance judgments.   The total number of entities in the missed vital annotations is  28 Wikipedia and 7  Twitter, making a total of 35. The  great majority (86.7\%) of the documents are social. This suggests that social (tweets and blogs) can talk about the entities without mentioning  them by name more than news and others do. This is, of course, inline with intuition. 

%    

%    

%    \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*}

%    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 stage of entity-based filtering and ranking pipeline. Specifically 

we conducted experiments to study the impacts of cleansing, 

entity profiles, relevance ratings, categories of documents, entity profiles. We also measured  impact of the different factors and choices  on later stages of the pipeline. 

Experimental results show that cleansing can remove entire or parts of the content of 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 difference between the cleansed and raw ranges from  6.8\% t 26.2\%. These increases, in actual document-entity pairs,  is in thousands. We believe this is a substantial increase. However, the recall increases do not always translate to improved F-score in overall performance.  In the vital relevance ranking for both Wikipedia and aggregate 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-variant, though the difference is negligible.  However, for vital-relevant, the raw corpus performs  better across all entity profiles and entity types except in partial canonical names of Wikipedia entities. 

The use of different profiles also shows a big difference in recall. Except in the case of Wikipedia where the use of canonical partial achieves better than name-variant, there is a steady increase in recall from canonical to  canonical partial, to name-variant, and to name-variant partial. This pattern is also observed across the document categories.  However, here too, the relationship between   the gain in recall as we move from less richer profile to a more richer profile and overall performance as measured by F-score  is not linear. 

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 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. 2) Taking both performance (recall at filtering and overall F-score during evaluation) into account, 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(18.3\%).  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. 

The high recall and subsequent higher overall performance of Wikipedia entities can  be due to two reasons. 1) Wikipedia entities are relatively well described than Twitter entities. The fact that we can retrieve different name variants from DBpedia is a measure of relatively rich description. Rich description plays a role in both filtering and computation of features such as similarity measures in later stages of the pipeline.   By contrast, we have only two names for Twitter entities: their user names and their display names which we collect from their Twitter pages. 2) There is not DBpedia-like resource for Twitter entities from which alternative names cane be collected.   

In the experimental results, we also observed that recall scores in the vital category are higher than in the relevant category. This observation  confirms one commonly held assumption:(frequency) mention is related to relevance.  this is the assumption why term frequency is used an indicator of document relevance in many information retrieval systems. The more  a document mentions an entity explicitly by name, the more likely the document is vital to the entity.

Across document categories, we observe a pattern in recall of others, followed by news, and then by social. Social documents are the hardest to retrieve. This can be explained by the fact that social documents (tweets and  blogs) are more likely to point to a resource where the entity is mentioned, mention the entities with some short abbreviation, or talk without mentioning the entities, but with some context in mind. By contrast news documents mention the entities they talk about using the common name variants more than social documents do. However, the greater difference in percentage recall between the different entity profiles in the news category indicates news refer to a given entity with different names, rather than by one standard name. By contrast others show least variation in referring to news. Social documents falls in between the two.  The deltas, for Wikipedia entities, between canonical partials and canonicals,  and name-variants and canonicals are high, an indication that canonical partials and name-variants bring in new relevant documents that can not be retrieved by canonicals. The rest of the two deltas are very small,  suggesting that partial names of name variants do not bring in new relevant documents. 

\section{Unfilterable documents}

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. 

% 

 We observed that there are vital-relevant documents that we miss from raw only, and similarly from cleansed only. The reason for this is transformation from one format to another. The most interesting documents are those that we miss from both raw and cleansed corpus. We first identified the number of KB entities who have a vital relevance judgment and  whose documents can not be retrieved (they were 35 in total) and conducted a manual examination into their content to find out why they are missing. 

 We  observed  that among the missing documents, different document ids can have the same content, and be judged multiple times for a given entity.  %In the vital annotation, there are 88 news, and 409 weblog. 

 Avoiding duplicates, we randomly selected 35 documents, one for each entity.   The documents are 13 news and  22  social. Here below we have classified the situation under which a document can be vital for an entity without mentioning the entities with the different entity  profiles we used for filtering. 

 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.  

\section{Conclusions}

In this paper, we examined the filtering stage of the entity-centric stream filtering and ranking  by holding the later stages of fixed. 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 relevance ratings. We attempted to address the following research questions: 1) does cleansing affect filtering and subsequent performance? 2) what is the most effective way of entity profiling? 3) is filtering different for Wikipedia and Twitter entities? 4) are some type of documents easily filterable and others not? 5) does a gain in recall at filtering step translate to a gain in F-measure at the end of the pipeline? and 6) what are the circumstances under which vital documents can not be retrieved? 

Cleansing does remove parts or entire contents of documents making them irretrievable. However, because of the introduction of false positives, recall gains by  raw corpus and some  richer entity profiles do not necessarily translate to overall performance gain. The results conclusion on this is mixed in the sense that cleansing helps improve the recall on vital documents and Wikipedia entities, but reduces the recall on Twitter entities and the relative category of relevance ranking. Vital and relevant documents show a difference in retrieval nonperformance documents are easier to filter than relevant.  

Despite an aggressive attempt to filter as many vital-relevant documents as possible,  we observe that there are still documents that we miss. While some are possible to retrieve with some modifications, some others are not. There are some document that indicate that an information filtering system does not seem to get them no matter how rich representation of entities they use. These circumstances under which this happens are many. We found that some documents have no content at all, subjectivity(it is not clear why some are judged vital). However, the main circumstances under which vital  documents can defy filtering is: outgoing link mentions, 

venue-event, entity - related entity, organization - main area of operation, entity - group, artist - artist's work,  party-politician, and world knowledge.  

%ACKNOWLEDGMENTS are optional

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