HCDA/cikm-paper Changeset - 92ad5e1cd132 · Centrum Wiskunde & Informatica (CWI)

Changeset - 92ad5e1cd132

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Arjen de Vries (arjen) - 11 years ago 2014-06-12 07:02:38
arjen.de.vries@cwi.nl

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@@ @@ -1063,8 +1063,17 @@ step. @@
 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.
 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 no standard
 DBpedia-like resource for Twitter entities, from which alternative
 names can 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.
 %\section{Unfilterable documents}\label{sec:unfil}
-\section{Missing vital-relevant documents}\label{sec:unfil}
+\section{Vital or Relevant but Missing?!}\label{sec:unfil}
+%
- 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.
+ The use of name-variant partial for filtering is an exhaustive 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}
+\begin{tabular}{l@{\quad}rrrrrr}
 \hline
 \multicolumn{1}{l}{\rule{0pt}{12pt}category}&\multicolumn{1}{l}{\rule{0pt}{12pt}Vital }&\multicolumn{1}{l}{\rule{0pt}{12pt}Relevant }&\multicolumn{1}{l}{\rule{0pt}{12pt}Total }\\[5pt]
 \hline

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