Name Extraction and Formal Concept Analysis
Identifieur interne : 000386 ( Main/Exploration ); précédent : 000385; suivant : 000387Name Extraction and Formal Concept Analysis
Auteurs : Kazem Taghva [États-Unis] ; Russell Beckley [États-Unis] ; Jeffrey Coombs [États-Unis]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2011.
Abstract
Abstract: Many applications of Formal Concept Analysis (FCA) start with a set of structured data such as objects and their properties. In practice, most of the data which is readily available are in the form of unstructured or semistructured text. A typical application of FCA assumes the extraction of objects and their properties by some other methods or techniques. For example, in the 2003 Los Alamos National Lab (LANL) project on Advanced Knowledge Integration In Assessing Terrorist Threats, a data extraction tool was used to mine the text for the structured data. In this paper, we provide a detailed description of our approach to extraction of personal names for possible subsequent use inFCA. Our basic approach is to integrate statistics on names and other words into an adaptation of a Hidden Markov Model (HMM). We use lists of names and their relative frequencies compiled from U.S. Census data. We also use a list of non-name words along with their frequencies in a training set from our collection of documents. These lists are compiled into one master list to be used as a part of the design.
Url:
DOI: 10.1007/978-3-642-22688-5_28
Affiliations:
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In practice, most of the data which is readily available are in the form of unstructured or semistructured text. A typical application of FCA assumes the extraction of objects and their properties by some other methods or techniques. For example, in the 2003 Los Alamos National Lab (LANL) project on Advanced Knowledge Integration In Assessing Terrorist Threats, a data extraction tool was used to mine the text for the structured data. In this paper, we provide a detailed description of our approach to extraction of personal names for possible subsequent use inFCA. Our basic approach is to integrate statistics on names and other words into an adaptation of a Hidden Markov Model (HMM). We use lists of names and their relative frequencies compiled from U.S. Census data. We also use a list of non-name words along with their frequencies in a training set from our collection of documents. These lists are compiled into one master list to be used as a part of the design.</div></front></TEI><affiliations><list><country><li>États-Unis</li></country><region><li>Nevada</li></region><settlement><li>Las Vegas</li></settlement></list><tree><country name="États-Unis"><region name="Nevada"><name sortKey="Taghva, Kazem" sort="Taghva, Kazem" uniqKey="Taghva K" first="Kazem" last="Taghva">Kazem Taghva</name></region><name sortKey="Beckley, Russell" sort="Beckley, Russell" uniqKey="Beckley R" first="Russell" last="Beckley">Russell Beckley</name><name sortKey="Coombs, Jeffrey" sort="Coombs, Jeffrey" uniqKey="Coombs J" first="Jeffrey" last="Coombs">Jeffrey Coombs</name><name sortKey="Taghva, Kazem" sort="Taghva, Kazem" uniqKey="Taghva K" first="Kazem" last="Taghva">Kazem Taghva</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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