diff --git a/mypaper-final.tex b/mypaper-final.tex index 2908c42ec266f745c41cc80928542dfc8da4fed0..4a5c5135b464d6ce878882b4d6ef597dc24b914b 100644 --- a/mypaper-final.tex +++ b/mypaper-final.tex @@ -127,8 +127,8 @@ 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 -the filtering stage by examing their contents. This way, we give -estimate a practical upper-bound of recall for entity-centric stream +the filtering stage by examining their contents. This way, we give +estimate of a practical upper-bound of recall for entity-centric stream filtering. \end{abstract} @@ -497,7 +497,7 @@ The tables in \ref{tab:name} and \ref{tab:source-delta} show recall for Wikipedi \section{Impact on classification} -% In the overall experimental setup, classification, ranking, and evaluationn are kept constant. + In the overall experimental setup, classification, ranking, and evaluationn are kept constant. Following \cite{balog2013multi} settings, we use WEKA's\footnote{http://www.cs.waikato.ac.nz/∼ml/weka/} Classification Random Forest. Features we use incude similarity features such as cosine and jaccard, document-entity features such as docuemnt mentions entity in title, in body, frequency of mention, etc., and related entity features such as page rank scores. In total we sue The features consist of similarity measures between the KB entiities profile text, document-entity features such as 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)} @@ -713,12 +713,13 @@ Wikipedia's canonical partial is the best entity profile for Wikipedia entities. -<<<<<<< HEAD +<<<<<<< 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. -======= -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. ->>>>>>> 60fbfbab0287ab72519987bdcba3adb5a0aa93c8 +======= +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. +>>>>>>> 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.