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 <channel rdf:about="http://ebiquity.umbc.edu/tag/text classification/">
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    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/460/Improving-Binary-Classification-on-Text-Problems-using-Differential-Word-Features"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/383/Wikipedia-as-an-Ontology-for-Describing-Documents"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/235/A-Bayesian-Methodology-towards-Automatic-Ontology-Mapping"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/232/Yahoo-as-an-Ontology-using-Yahoo-categories-to-describe-documents-"/>
    <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/166/Learning-the-Semantic-Meaning-of-a-Concept-from-the-Web"/>
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 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/460/Improving-Binary-Classification-on-Text-Problems-using-Differential-Word-Features">
  <title><![CDATA[Improving Binary Classification on Text Problems using Differential Word Features]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/460/Improving-Binary-Classification-on-Text-Problems-using-Differential-Word-Features</link>
  <description><![CDATA[We describe an efficient technique to weigh word-based features in binary classification tasks and show that it significantly improves classification accuracy on a range of problems. The most common text classification approach uses a document's ngrams (words and short phrases) as its features and assigns feature values equal to their frequency or TFIDF score relative to the training corpus. Our approach uses values computed as the product of an ngram's document frequency and the difference o...]]></description>
  <dc:date>2009-11-02</dc:date>
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 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/383/Wikipedia-as-an-Ontology-for-Describing-Documents">
  <title><![CDATA[Wikipedia as an Ontology for Describing Documents]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/383/Wikipedia-as-an-Ontology-for-Describing-Documents</link>
  <description><![CDATA[Identifying topics and concepts associated with a set of documents is a task common to many applications. It can help in the annotation and categorization of documents and be used to model a person's current interests for improving search results, business intelligence or selecting appropriate advertisements.  One approach is to associate a document with a set of topics selected from a fixed ontology or vocabulary of terms. We have investigated using Wikipedia's articles and associated pages ...]]></description>
  <dc:date>2008-03-31</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/235/A-Bayesian-Methodology-towards-Automatic-Ontology-Mapping">
  <title><![CDATA[A Bayesian Methodology towards Automatic Ontology Mapping]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/235/A-Bayesian-Methodology-towards-Automatic-Ontology-Mapping</link>
  <description><![CDATA[This paper presents our ongoing effort on developing a principled methodology for automatic ontology mapping based on BayesOWL, a probabilistic framework we developed for modeling uncertainty in semantic web. The pro-posed method includes four components: 1) learning prob-abilities (priors about concepts, conditionals between sub-concepts and superconcepts, and raw semantic similarities between concepts in two different ontologies) using Naive Bayes text classification technique, by explicitl...]]></description>
  <dc:date>2005-07-09</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/232/Yahoo-as-an-Ontology-using-Yahoo-categories-to-describe-documents-">
  <title><![CDATA[Yahoo as an Ontology - using Yahoo categories to describe documents,]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/232/Yahoo-as-an-Ontology-using-Yahoo-categories-to-describe-documents-</link>
  <description><![CDATA[We suggest that one (or a collection) of names of
Yahoo! (or any other WWW indexer's) categories
can be used to describe the content of a document.
Such categories offer a standardized and universal
way for referring to or describing the nature of real
world objects, activities, documents and so on, and
may be used (we suggest) to semantically characterize
the content of documents. WWW indices,
like Yahoo! provide a huge hierarchy of categories
(topics) that touch every aspect of hum...]]></description>
  <dc:date>1999-11-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/166/Learning-the-Semantic-Meaning-of-a-Concept-from-the-Web">
  <title><![CDATA[Learning the Semantic Meaning of a Concept from the Web]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/166/Learning-the-Semantic-Meaning-of-a-Concept-from-the-Web</link>
  <description><![CDATA[Many researchers have applied text classification techniques to the ontology mapping problem. The mapping results in these researches heavily depend on the availability of highly relevant text exemplars associated with individual concepts. However, manual preparation of exemplars is costly. In this work, we propose to automatically collect text exemplars by downloading and processing web pages listed in the search results obtained by querying a search engine. Search queries are formed for eac...]]></description>
  <dc:date>2006-08-03</dc:date>
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