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.

%% TODO: PERHAPS SUMMARY OF DISCUSSION HERE

\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.

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

(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

\paragraph*{kl} 

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

  \paragraph*{PPR}

For each entity, we computed a PPR score from

a Wikipedia snapshot  and we kept the top 100  entities along

with the corresponding scores.

\paragraph*{Surface Form (sForm)}

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

are redirects, labels and names.

\paragraph*{Context (contxL, contxR)}

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.