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<!--
	This ontology document is licensed under the Creative Commons
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 <channel rdf:about="http://ebiquity.umbc.edu//tag/html/text classification/?t=text+classification">
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  <link><![CDATA[http://ebiquity.umbc.edu//tag/html/text classification/?t=text+classification]]></link>
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    <rdf:Seq>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/381/Domain-Independent-Sentiment-Analysis"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/166/Learning-the-Semantic-Meaning-of-a-Concept-from-the-Web"/>
      <rdf:li resource="http://ebiquity.umbc.edu/project/html/id/29/UMBC-OntoMapper-A-Tool-For-Mapping-Between-Two-Ontologies"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/659/Meerkat-Mafia-Multilingual-and-Cross-Level-Semantic-Textual-Similarity-systems"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/580/Identifying-and-Isolating-Text-Classification-Signals-from-Domain-and-Genre-Noise-for-Sentiment-Analysis"/>
      <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/377/Learning-the-Semantic-Meaning-of-a-Concept-from-the-Web"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/249/A-Bayesian-Network-Approach-to-Ontology-Mapping"/>
      <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/resource/html/id/195/Learning-the-Semantic-Meaning-of-a-Concept-from-the-Web"/>
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 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/381/Domain-Independent-Sentiment-Analysis">
  <title><![CDATA[Domain Independent Sentiment Analysis]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/381/Domain-Independent-Sentiment-Analysis</link>
  <description><![CDATA[Domain independent sentiment signals are words or word pairs that are present and have the same sentimental orientation in multiple domains. These words can be easily identified if you have an accurate representation of their in-domain sentimental orientation. If you also have an accurate representation of their sentimental strength then you can use them to correctly classify out of domain documents with reasonable accuracy. In this talk I will present a method to identify domain independent ...]]></description>
  <dc:date>2011-03-01</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>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/project/html/id/29/UMBC-OntoMapper-A-Tool-For-Mapping-Between-Two-Ontologies">
  <title><![CDATA[UMBC OntoMapper: A Tool For Mapping Between Two Ontologies]]></title>
  <link>http://ebiquity.umbc.edu/project/html/id/29/UMBC-OntoMapper-A-Tool-For-Mapping-Between-Two-Ontologies</link>
  <description><![CDATA[Forcing all communicating agents to share a common ontology is infeasible. A group of
people with similar interests usually has its own organizational schemes for documents. This
organization may be in the form of an ontology. Different agents may define very different
ontologies, and the semantics for the same terms may be very different in their ontologies. A
mapping from one agent's ontology to another agent's ontology is required to facilitate
communication between agents.
   Thi...]]></description>
  <dc:date>2001-09-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/659/Meerkat-Mafia-Multilingual-and-Cross-Level-Semantic-Textual-Similarity-systems">
  <title><![CDATA[Meerkat Mafia: Multilingual and Cross-Level Semantic Textual Similarity systems]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/659/Meerkat-Mafia-Multilingual-and-Cross-Level-Semantic-Textual-Similarity-systems</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 TF-IDF score relative to the training corpus. Our approach uses values computed as the product of an ngram's document frequency and the difference ...]]></description>
  <dc:date>2014-08-23</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/580/Identifying-and-Isolating-Text-Classification-Signals-from-Domain-and-Genre-Noise-for-Sentiment-Analysis">
  <title><![CDATA[Identifying and Isolating Text Classification Signals from Domain and Genre Noise for Sentiment Analysis]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/580/Identifying-and-Isolating-Text-Classification-Signals-from-Domain-and-Genre-Noise-for-Sentiment-Analysis</link>
  <description><![CDATA[Sentiment analysis is the automatic detection and measurement of sentiment in text segments by machines. This problem is generally divided into three tasks: a sentiment detection task, a topic detection task, and a sentiment measurement task. The first task attempts to determine whether the author is being objective or whether they are expressing a value judgment on the topic. The second task attempts to determine the topic of the sentiment. The third task attempts to determine whether the au...]]></description>
  <dc:date>2011-12-05</dc:date>
 </item>
 <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 TF-IDF score relative to the training corpus. Our approach uses values computed as the product of an ngram's document frequency and the difference ...]]></description>
  <dc:date>2009-11-02</dc:date>
 </item>
 <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/377/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/paper/html/id/377/Learning-the-Semantic-Meaning-of-a-Concept-from-the-Web</link>
  <description><![CDATA[Many researchers have used text classification method in solving the ontology mapping problem. Their mapping results heavily depend on the availability of quality exemplars used as training data. However, manual preparation of exemplars is costly. In this work, we propose to automatically extract text from web pages returned by a search engine. Search queries are formed according to the semantic information given in the ontology. We have implemented a prototype system that automates the entir...]]></description>
  <dc:date>2007-05-28</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/249/A-Bayesian-Network-Approach-to-Ontology-Mapping">
  <title><![CDATA[A Bayesian Network Approach to Ontology Mapping]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/249/A-Bayesian-Network-Approach-to-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. In this approach, the source and target ontologies are first translated into Bayesian networks (BN); the concept mapping between the two ontologies are treated as evidential reasoning between the two translated BN. Probabilities needed for constructing conditional probability tables (CPT...]]></description>
  <dc:date>2005-11-06</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/resource/html/id/195/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/resource/html/id/195/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>
 </item>
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