Text segmentation using a cache memory
Identifieur interne : 008D55 ( Main/Merge ); précédent : 008D54; suivant : 008D56Text segmentation using a cache memory
Auteurs : B. Bigi [France] ; R. Demori [France]Source :
- Control and intelligent systems [ 1480-1752 ] ; 2002.
Descripteurs français
- Pascal (Inist)
English descriptors
- KwdEn :
Abstract
This article describes the application of an information-theoretic approach to document segmentation. Several segmentation criteria are proposed using topic shift detection or just blindly comparing the contents of cache memories where keywords are temporarily stored as a document is analyzed. Experiments with a large corpus of articles from the French newspaper Le Monde show tangible advantages when different models are combined with a suitable strategy. Experimental results show that different strategies for topic shift detection have to be used depending on whether high recall or high precision is sought. Furthermore, methods based on topic-independent distributions provide complementary candidates with respect to the use of topic-dependent distributions, leading to an increase in recall with a minor loss in precision.
Links toward previous steps (curation, corpus...)
- to stream PascalFrancis, to step Corpus: 000824
- to stream PascalFrancis, to step Curation: 000219
- to stream PascalFrancis, to step Checkpoint: 000770
Links to Exploration step
Pascal:03-0116906Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en" level="a">Text segmentation using a cache memory</title><author><name sortKey="Bigi, B" sort="Bigi, B" uniqKey="Bigi B" first="B." last="Bigi">B. Bigi</name><affiliation wicri:level="3"><inist:fA14 i1="01"><s1>LORIA, University of Nancy I, BP 239</s1><s2>54506 Vandœuvre-Lès-Nancy</s2><s3>FRA</s3><sZ>1 aut.</sZ></inist:fA14><country>France</country><placeName><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region><settlement type="city">Vandœuvre-lès-Nancy</settlement></placeName></affiliation></author><author><name sortKey="Demori, R" sort="Demori, R" uniqKey="Demori R" first="R." last="Demori">R. Demori</name><affiliation wicri:level="3"><inist:fA14 i1="02"><s1>LIA CERI-IUP, University of Avignon, BP 1228</s1><s2>84911 Avignon</s2><s3>FRA</s3><sZ>2 aut.</sZ></inist:fA14><country>France</country><placeName><region type="region" nuts="2">Provence-Alpes-Côte d'Azur</region><settlement type="city">Avignon</settlement></placeName></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">INIST</idno><idno type="inist">03-0116906</idno><date when="2002">2002</date><idno type="stanalyst">PASCAL 03-0116906 CRAN</idno><idno type="RBID">Pascal:03-0116906</idno><idno type="wicri:Area/PascalFrancis/Corpus">000824</idno><idno type="wicri:Area/PascalFrancis/Curation">000219</idno><idno type="wicri:Area/PascalFrancis/Checkpoint">000770</idno><idno type="wicri:explorRef" wicri:stream="PascalFrancis" wicri:step="Checkpoint">000770</idno><idno type="wicri:doubleKey">1480-1752:2002:Bigi B:text:segmentation:using</idno><idno type="wicri:Area/Main/Merge">008D55</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a">Text segmentation using a cache memory</title><author><name sortKey="Bigi, B" sort="Bigi, B" uniqKey="Bigi B" first="B." last="Bigi">B. Bigi</name><affiliation wicri:level="3"><inist:fA14 i1="01"><s1>LORIA, University of Nancy I, BP 239</s1><s2>54506 Vandœuvre-Lès-Nancy</s2><s3>FRA</s3><sZ>1 aut.</sZ></inist:fA14><country>France</country><placeName><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region><settlement type="city">Vandœuvre-lès-Nancy</settlement></placeName></affiliation></author><author><name sortKey="Demori, R" sort="Demori, R" uniqKey="Demori R" first="R." last="Demori">R. Demori</name><affiliation wicri:level="3"><inist:fA14 i1="02"><s1>LIA CERI-IUP, University of Avignon, BP 1228</s1><s2>84911 Avignon</s2><s3>FRA</s3><sZ>2 aut.</sZ></inist:fA14><country>France</country><placeName><region type="region" nuts="2">Provence-Alpes-Côte d'Azur</region><settlement type="city">Avignon</settlement></placeName></affiliation></author></analytic><series><title level="j" type="main">Control and intelligent systems</title><title level="j" type="abbreviated">Control intell. syst.</title><idno type="ISSN">1480-1752</idno><imprint><date when="2002">2002</date></imprint></series></biblStruct></sourceDesc><seriesStmt><title level="j" type="main">Control and intelligent systems</title><title level="j" type="abbreviated">Control intell. syst.</title><idno type="ISSN">1480-1752</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Cache memory</term><term>Detection</term><term>Memory effect</term><term>Modeling</term><term>Segmentation</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Effet mémoire</term><term>Segmentation</term><term>Détection</term><term>Antémémoire</term><term>Modélisation</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">This article describes the application of an information-theoretic approach to document segmentation. Several segmentation criteria are proposed using topic shift detection or just blindly comparing the contents of cache memories where keywords are temporarily stored as a document is analyzed. Experiments with a large corpus of articles from the French newspaper Le Monde show tangible advantages when different models are combined with a suitable strategy. Experimental results show that different strategies for topic shift detection have to be used depending on whether high recall or high precision is sought. Furthermore, methods based on topic-independent distributions provide complementary candidates with respect to the use of topic-dependent distributions, leading to an increase in recall with a minor loss in precision.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Grand Est</li><li>Lorraine (région)</li><li>Provence-Alpes-Côte d'Azur</li></region><settlement><li>Avignon</li><li>Vandœuvre-lès-Nancy</li></settlement></list><tree><country name="France"><region name="Grand Est"><name sortKey="Bigi, B" sort="Bigi, B" uniqKey="Bigi B" first="B." last="Bigi">B. Bigi</name></region><name sortKey="Demori, R" sort="Demori, R" uniqKey="Demori R" first="R." last="Demori">R. Demori</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Wicri/Lorraine/explor/InforLorV4/Data/Main/Merge
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 008D55 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Main/Merge/biblio.hfd -nk 008D55 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Wicri/Lorraine
|area= InforLorV4
|flux= Main
|étape= Merge
|type= RBID
|clé= Pascal:03-0116906
|texte= Text segmentation using a cache memory
}}
| This area was generated with Dilib version V0.6.33. |  |