\usepackage{url}

\numberofauthors{8} %  in this sample file, there are a *total*

filtering.

 pipeline out of a big collection of stream documents. Filtering  sifts  an incoming information for information relevant to user profiles \cite{robertson2002trec}. In the specific setting of CCR, these profiles are

entities' page to using anchor texts from other Wikipedia pages, and from

The rest of the paper  is organized as follows. Section \ref{sec:desc} describes the dataset and section \ref{sec:fil} defines the task. In section  \ref{sec:lit}, we discuss related litrature folowed by a discussion of our method in \ref{sec:mthd}. Following that,  we present the experimental resulsy in \ref{sec:expr}, and discuss and analyze them in \ref{sec:analysis}. Towards the end, we discuss the impact of filtering choices on classification in section \ref{sec:impact}, examine and categorize unfilterable docuemnts in section \ref{sec:unfil}. Finally, we present our conclusions in \ref{}{sec:conc}.

 \section{Data Description}\label{sec:desc}

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

\footnote{\url{http://trec-kba.org/trec-kba-2013.shtml}}

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

\footnote{\url{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{\url{http://trec-kba.org/kba-stream-corpus-2012.shtml}},

arxiv\footnote{\url{http://arxiv.org/}}, and

spinn3r\footnote{\url{http://spinn3r.com/}}.

Table \ref{tab:streams} shows the sources, the number of hourly

directories, and the number of chunk files.

 \section{Stream Filtering}\label{sec:fil}

\section{Literature Review} \label{sec:lit}

\section{Method}\label{sec:mthd}

We build entity profiles for the KB entities of interest. We have two

types: Twitter and Wikipedia. Both entities have been selected, on

purpose by the track organisers, to occur only sparsely and be less-documented.

For the Wikipedia entities, we fetch different name variants

from DBpedia: name, label, birth name, alternative names,

redirects, nickname, or alias. 

These extraction results are summarized in Table

\ref{tab:sources}.

For the Twitter entities, we visit

their respective Twitter pages and fetch their display names. 

The collection contains a total number of 121 Wikipedia entities.

Every entity has a corresponding DBpedia label.  Only 82 entities have

a name string and only 49 entities have redirect strings. (Most of the

entities have only one string, except for a few cases with multiple

redirect strings; Buddy\_MacKay, has the highest (12) number of

redirect strings.) 

We combine the different name variants we extracted to form a set of

strings for each KB 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 entity\\

\url{http://en.wikipedia.org/wiki/Benjamin_Bronfman}\\

Benjamin Bronfman is a canonical name of the entity. 

An example is given in Table \ref{tab:profile}.

From the combined name variants and

the canonical names, we  created four sets of profiles for each

entity: canonical(cano) canonical partial (cano-part), all name

variants combined (all) and partial names of all name

variants(all-part). We refer to the last two profiles as name-variant

and name-variant partial. The names in parentheses are used in table

captions.

%Most (more than 80\%) of the annotation documents are in the test set.

The 2013 training and test data contain 68405

annotations, of which 50688 are unique document-entity pairs.   Out of

these, 24162 unique document-entity pairs are vital (9521) or relevant

(17424).

\section{Experiments and Results}\label{sec:expr}

When comparing recall for vital and relevant, we observe that

canonical names are more effective for vital than for relevant

entities, in particular for the 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 conclude that the most relevant documents mention the

entities by their common name variants.

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 ranges from 76.4\% to 98.4\%, the recall for social

documents ranges 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.

Overall, across all entities types and all entity profiles, documents

Table \ref{tab:name} summarizes the differences between Wikipedia and

Twitter entities.  Wikipedia entities' canonical representation

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.

In Twitter entities, recall of canonical matching is very low.%

\footnote{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 identical.}

%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 a higher recall

for Wikipedia than for Twitter entities. 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. 

Section \ref{sec:analysis} discusses the most plausible explanations for these findings.

\section{Impact on classification}\label{sec:impact}

In the overall experimental setup, classification, ranking, and

evaluation are kept constant. Following \cite{balog2013multi}

settings, we use

WEKA's\footnote{\url{http://www.cs.waikato.ac.nz/~ml/weka/}} Classification

Random Forest. However, we use fewer numbers of features which we

found to be more effective. We determined the effectiveness of the

features by running the classification algorithm using the fewer

features we implemented and their features. Our feature

implementations achieved better results.  The total numbers of

features we used are 13 and are listed below.

There are 3 interesting observations: 

1) cleansing impacts Twitter

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. 

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}\label{sec:unfil}

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

% 

 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.  

\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. 

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. 

\paragraph*{Event place - Event} A document that talks about an event is vital to the location entity where it takes place.  For example Maha Music Festival takes place in Lewis and Clark\_Landing, and a document talking about the festival is vital for the park. There are also cases where an event's address places the event in a park and due to that the document becomes vital to the park. This is basically being mentioned by address which belongs to alarger space. 

\paragraph*{Disagreement} For a few remaining documents, the authors disagree with the assessors as to why these are vital to the entity.

\section{Conclusions} \label{sec:conc}