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% Adrienne Griscti ---> griscti@acm.org

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% Questions/suggestions regarding the guidelines, .tex and .cls files, etc. to

% Gerald Murray ---> murray@hq.acm.org

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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}

%

% You need the command \numberofauthors to handle the 'placement

% and alignment' of the authors beneath the title.

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}

We base this analysis on the TREC-KBA 2013 dataset%

that consists of three main parts: a time-stamped stream corpus, a set of

KB entities to be curated, and a set of relevance judgments. A CCR

system now has to identify for each KB entity which documents in the

stream corpus are to be considered by the human curator.

\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

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

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 \\

achieves a recall of 70\%, while 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\%.

%This 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: documents have already been extracted by the different

%name variants and thus using their partial names do not bring in many

%new relevant documents.

For Wikipedia entities, canonical

partial achieves better recall than name-variant in both the cleansed and

the raw corpus.  %In the raw extraction, the difference is about 3.7.

and canonical partial are the same for Twitter entities because they

are one word strings. For example in https://twitter.com/roryscovel,

%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\%.

%% TODO: PERHAPS SUMMARY OF DISCUSSION HERE

\section{Impact on classification}

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 

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

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

  KL-divergence between the document and the entity's Wikipedia page

For each entity, we computed a PPR score from

a Wikipedia snapshot  and we kept the top 100  entities along

with the corresponding scores.

For each Wikipedia entity, we gathered DBpedia name variants. These

are redirects, labels and names.

From the WikiLink corpus \cite{singh12:wiki-links}, we collected

all left and right contexts (2 sentences left and 2 sentences

right) and generated n-grams between uni-grams and quadro-grams

for each left and right context. 

Finally,  we select  the 5 most frequent n-grams for each context.

  Term position of the first occurrence of the target entity in the document 

  body 

  Term position of the last occurrence of the target entity in the document body

  Term position of the first occurrence of the target entity in the document 

  body normalised by the document length 

  No. of occurrences of the target entity in the  document body

  Features we use incude similarity features such as cosine and jaccard, document-entity features such as docuemnt mentions entity in title, in body, frequency  of mention, etc., and related entity features such as page rank scores. In total we sue  The features consist of similarity measures between the KB entiities profile text, document-entity features such as  

  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 max-F. 

\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

% 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*}

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.