HCDA/cikm-paper Changeset - 7babd96c03f9 · Centrum Wiskunde & Informatica (CWI)

Changeset - 7babd96c03f9

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Gebrekirstos Gebremeskel - 11 years ago 2014-06-12 05:21:03
destinycome@gmail.com

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 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)}
 This is a mapping of strings to Wikipedia concepts and vice versa
 \cite{spitkovsky2012cross}.
 The GCLD corpus estimates two probabilities:
 (1) the probability with which a string is used as anchor text to
 a Wikipedia entity
 %thus distributing the probability mass over the different entities that it is used as anchor text;
 and (2) the
 probability that indicates the strength of co-reference of an anchor with respect to other anchors to  a given Wikipedia entity.  We use the product of both for each string.
 \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.

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