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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 

}

%SUGGESTION:

%\title{The Impact of Entity-Centric Stream Filtering on Recall and

%  Missed Documents}

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Nevertheless, this step has a large impact on the recall that can be

maximally attained! Therefore, in this study, we have focused on just

this filtering stage and conduct an in-depth analysis of the main design

decisions here: how to cleans the noisy text obtained online, 

the methods to create entity profiles, the

types of entities of interest, document type, and the grade of

relevance of the document-entity pair under consideration.

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

corresponding system components) affect filtering performance.

We identify and characterize the relevant documents that do not pass

the filtering stage by examing their contents. This way, we

estimate a practical upper-bound of recall for entity-centric stream

\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

performance. The main contribution of the

paper are an in-depth analysis of the factors that affect entity-based

stream filtering, identifying optimal entity profiles without

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: 

\textbf{TODO!!}

 \section{Data 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 \\

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}

All analyses in this paper are carried out on the documents that have

relevance assessments associated to them. For this purpose, we

extracted those documents from the big corpus. We experiment with all

KB entities. For each KB entity, we extract different name variants

from DBpedia and Twitter.

\

\subsection{Entity Profiling}

\begin{table}

\caption{Number of different DBpedia name variants}

\begin{center}

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

 Name variant& No. of strings  \\

\hline

 Name  &82\\

 Label   &121\\

Redirect  &49 \\

 Birth Name &6\\

 Nickname & 1&\\

 Alias &1 \\

 Alternative Names &4\\

\hline

\end{tabular}

\end{center}

\label{tab:sources}

\end{table}

\begin{table*}

\caption{Example entity profiles (upper part Wikipedia, lower part Twitter)}

\begin{center}

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

 &Wikipedia&Twitter \\

\hline

 &Benjamin\_Bronfman& roryscovel\\

  cano&[Benjamin Bronfman] &[roryscovel]\\

  cano-part &[Benjamin, Bronfman]&[roryscovel]\\

  all&[Ben Brewer, Benjamin Zachary Bronfman] &[Rory Scovel] \\

\hline

\end{tabular}

\end{center}

\label{tab:breakdown}

\end{table}

\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{Percentage of vital or relevant documents retrieved under different name variants (upper part from cleansed, lower part from raw)}

\begin{center}

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

\hline

\label{tab:source-delta}

\end{table*}

%The break down of the raw corpus by document source category is presented in Table

%\ref{tab:source-delta}.  

 \subsection{ Relevance Rating: vital and relevant}

%  \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}

%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.

in the others category achieve a higher recall than news, and news documents, 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.

%%NOTE TABLE REMOVED:\\\\

%

%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

%delta between name-variant and canonical means 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}

%% TODO: PERHAPS SUMMARY OF DISCUSSION HERE

\section{Impact on classification}

\paragraph*{Google's Cross Lingual Dictionary (GCLD)}

This is a mapping of strings to Wikipedia concepts and vice versa

\cite{spitkovsky2012cross}. 

(1) the probability with which a string is used as anchor text to

a Wikipedia entity 

\paragraph*{jac} 

  Jaccard similarity between the document and the entity's Wikipedia page

\paragraph*{cos} 

  Cosine similarity between the document and the entity's Wikipedia page

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

There is a trade-off between using a richer entity-profile and retrieval of irrelevant documents. The richer the profile, the more relevant documents it retrieves, but also the more irrelevant documents. To put it into perspective, lets compare the number of documents that are retrieved with  canonical partial and with name-variant partial. Using the raw corpus, the former retrieves a total of 2547487 documents and achieves a recall of 72.2\%. By contrast, the later retrieves a total of 4735318 documents and achieves a recall of 90.2\%. The total number of documents extracted increases by 85.9\% for a recall gain of 18\%. The rest of the documents, that is 67.9\%, are newly introduced irrelevant documents. 

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

In vital ranking, across all entity profiles and types of corpus, Wikipedia's canonical partial  achieves better performance than any other Wikipedia entity profiles. In vital-relevant documents too, Wikipedia's canonical partial achieves the best result. In the raw corpus, it achieves a little less than name-variant partial. For Twitter entities, the name-variant partial profile achieves the highest F-score across all entity profiles and types of corpus.  

entities and relevant documents.  This  is validated by the

observation that recall  gains in Twitter entities and the relevant

categories in the raw corpus also translate into overall performance

gains. This observation implies that cleansing removes relevant and

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

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

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

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

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

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

most of the missing of the missing  docuemnts from cleansed are

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

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

transformation and cleasing processes. 

%%%% NEEDS WORK:

during evaluation) into account, there is a clear trade-off between using a richer entity-profile and retrieval of irrelevant documents. The richer the profile, the more relevant documents it retrieves, but also the more irrelevant documents. To put it into perspective, lets compare the number of documents that are retrieved with  canonical partial and with name-variant partial. Using the raw corpus, the former retrieves a total of 2547487 documents and achieves a recall of 72.2\%. By contrast, the later retrieves a total of 4735318 documents and achieves a recall of 90.2\%. The total number of documents extracted increases by 85.9\% for a recall gain of 18\%. The rest of the documents, that is 67.9\%, are newly introduced irrelevant documents. 

Wikipedia's canonical partial is the best entity profile for Wikipedia entities. This is interesting  to see that the retrieval of of  thousands vital-relevant document-entity pairs by name-variant partial does not translate to an increase in over all performance. It is even more interesting since canonical partial was not considered as contending profile for stream filtering by any of participant to the best of our knowledge. With this understanding, there  is actually no need to go and fetch different names variants from DBpedia, a saving of time and computational resources.

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

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

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