HCDA/cikm-paper Changeset - 2d75f50f60d9 · Centrum Wiskunde & Informatica (CWI)

Changeset - 2d75f50f60d9

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Arjen de Vries (arjen) - 11 years ago 2014-06-12 02:56:00
arjen.de.vries@cwi.nl

"done" with section 2 up to literature review

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mypaper-final.tex

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@@ @@ -214,9 +214,9 @@ The rest of the paper is is organized as follows: @@
 \textbf{TODO!!}
  \section{Data Description}
 We base this analysis on the TREC-KBA 2013 dataset
 \footnote{http://http://trec-kba.org/trec-kba-2013.shtml}. This dataset
 consists of three main parts: a time-stamped stream corpus, a set of
 We base this analysis on the TREC-KBA 2013 dataset%
 \footnote{http://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.
 %The results of the different entity profiles on the raw corpus are
 %broken down by source categories and relevance rank% (vital, or
 %relevant).
 In total, there are 24162 vital or relevant unique entity-document
 pairs. 9521 of them are vital  and  17424 are relevant. These
 documents  are categorized into 8 source categories: 0.98\% arxiv(a),
 .034\% classified(c), 0.34\% forum(f), 5.65\% linking(l), 11.53\%
 mainstream-news(m-n), 18.40\% news(n), 12.93\% social(s) and 50.2\%
 weblog(w). We have regrouped these source categories into three groups
 ``news'', ``social'', and ``other'', for two reasons: 1) some groups
 In total, the dataset contains 24162 unique entity-document
 pairs, vital or relevant; 9521 of these have been labelled as vital,
 and the remaining 17424 as relevant.
 All documents are categorized into 8 source categories: 0.98\%
 arxiv(a), 0.034\% classified(c), 0.34\% forum(f), 5.65\% linking(l),
 .53\% mainstream-news(m-n), 18.40\% news(n), 12.93\% social(s) and
 .2\% weblog(w). We have regrouped these source categories into three
 groups ``news'', ``social'', and ``other'', for two reasons: 1) some groups
 are very similar to each other. Mainstream-news and news are
 similar. The reason they exist separately, in the first place,  is
 because they were collected from two different sources, by different
 \% of all annotations.  The rest make up about 7\% and are all
 grouped as others.
  \section{Stream Filtering}
  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}. Its information needs are long-term and are
- reprsented persistent profiles  unlike the traditional search system
+ represented by persistent profiles, unlike the traditional search system
  whose adhoc information need is represented by a search
  query. Adaptive Filtering, one task of the filtering track,  starts
  with  a persistent user profile and a very small number of positive
@@ @@ -325,25 +324,25 @@ grouped as others. @@
  entities, and the relevance judgments are the small number of postive
  examples.
  Stream filtering: given a stream of documents of news items, blogs
  and social media on one hand and KB entities  on the other, filter
  the stream for  potentially relevant documents  such that the
  relevance classifier(ranker) achieves as maximum performance as
 Stream filtering is then the task to, given a stream of documents of news items, blogs
  and social media on one hand and a set of KB entities on the other,
  to filter the stream for  potentially relevant documents  such that
  the relevance classifier(ranker) achieves as maximum performance as
  possible.  Specifically, we conduct in-depth analysis on the choices
  and factors affecting the cleansing step, the entity-profile
  construction, the document category of the stream items, and the type
  of entities (Wikipedia or Twitter) , and finally their impact
  overall performance of the pipeline. Finally, we conduct manual
  examination of the vital documents that defy filtering. We strive to
  answer the following research questions:
  of entities (Wikipedia or Twitter) , and finally their impact overall
  performance of the pipeline. Finally, we conduct manual examination
  of the vital documents that defy filtering. We strive to answer the
  following research questions:
 \begin{enumerate}
   \item Does cleansing affect filtering and subsequent performance
   \item What is the most effective way of entity profile representation
   \item Is filtering different for Wikipedia and Twitter entities?
   \item Are some type of documents easily filterable and others not?
   \item Does a gain in recall at filtering step translate to a gain in F-measure at the end of the pipeline?
   \item What are the vital(relevant) documents that are not retrievable by a system?
   \item What characterizes the vital (and relevant) documents that are
     missed in the filtering step?
 \end{enumerate}
 The TREC filtering and the filtering as part of the entity-centric

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