\maketitle

\begin{abstract}

Cumulative citation recommendation refers to the problem faced by

knowledge base curators, who need to continuously screen the media for

updates regarding the knowledge base entries they manage. Automatic

system support for this entity-centric information processing problem

requires complex pipe\-lines involving both natural language

processing and information retrieval components. The default pipeline

involves four stages: filtering, classification, ranking (or scoring),

and evaluation. Filtering is an initial step, that reduces the

web-scale corpus of news and other relevant information sources that

may contain entity mentions into a working set of documents that should

be more manageable for the subsequent stages.

This step has a large impact on the recall that can be achieved.

Keeping the subsequent steps constant, we therefore zoom in into the

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

decisions here:

cleansing noisy web data, 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

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. 

%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. 

%% TODO: PERHAPS SUMMARY OF DISCUSSION HERE

\section{Impact on classification}

  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

%&\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 &max-F& 0.241&0.261&0.259&0.265\\

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

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

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

   twitter &max-F&0.105&0.105&0.218&0.228\\

%     &SU &0.105&0.250& 0.254&0.253\\

\hline

\hline

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

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

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:

2) Taking both performance (recall at filtering and overall F-score

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.

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

<<<<<<< HEAD

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. 

=======

>>>>>>> 60fbfbab0287ab72519987bdcba3adb5a0aa93c8

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}

\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.  

\begin{table}

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

\begin{center}

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

\hline