Study of Entity-Topic Models for OOV Proper Name Retrieval
Identifieur interne : 000341 ( Main/Exploration ); précédent : 000340; suivant : 000342Study of Entity-Topic Models for OOV Proper Name Retrieval
Auteurs : Imran Sheikh [France] ; Irina Illina [France] ; Dominique Fohr [France]Source :
English descriptors
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Abstract
Retrieving Proper Names (PNs) relevant to an audio documentcan improve speech recognition and content based audio-videoindexing. Latent Dirichlet Allocation (LDA) topic model hasbeen used to retrieve Out-Of-Vocabulary (OOV) PNs relevant toan audio document with good recall rates. However, retrieval ofOOV PNs using LDA is affected by two issues, which we studyin this paper: (1)Word Frequency Bias (less frequent OOV PNsare ranked lower); (2) Loss of Specificity (the reduced topicspace representation loses lexical context). Entity-Topic modelshave been proposed as extensions of LDA to specifically learnrelations between words, entities (PNs) and topics. We studyOOV PN retrieval with Entity-Topic models and show that theyare also affected by word frequency bias and loss of specificity.We evaluate our proposed methods for rare OOV PN re-rankingand lexical context re-ranking for LDA as well as for Entity-Topic models. The results show an improvement in both Recalland the Mean Average Precision.
Url:
Affiliations:
- France
- Grand Est, Lorraine (région)
- Metz, Nancy
- Institut national polytechnique de Lorraine, Université Nancy 2, Université de Lorraine
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Le document en format XML
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CS 25233 - 54052 Nancy cedex</addrLine><country key="FR"></country></address><ref type="url">http://www.univ-lorraine.fr/</ref></desc></org></tutelle><tutelle name="UMR7503" active="#struct-441569" type="indirect"><org type="institution" xml:id="struct-441569" status="VALID"><idno type="ISNI">0000000122597504</idno><idno type="IdRef">02636817X</idno><orgName>Centre National de la Recherche Scientifique</orgName><orgName type="acronym">CNRS</orgName><date type="start">1939-10-19</date><desc><address><country key="FR"></country></address><ref type="url">http://www.cnrs.fr/</ref></desc></org></tutelle></tutelles></hal:affiliation><country>France</country><placeName><settlement type="city">Nancy</settlement><settlement type="city">Metz</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Université de Lorraine</orgName></affiliation></author><author><name sortKey="Fohr, Dominique" sort="Fohr, Dominique" uniqKey="Fohr D" first="Dominique" last="Fohr">Dominique Fohr</name><affiliation wicri:level="1"><hal:affiliation type="researchteam" xml:id="struct-2359" status="OLD"><idno type="RNSR">200118295L</idno><orgName>Analysis, perception and recognition of speech</orgName><orgName type="acronym">PAROLE</orgName><desc><address><country key="FR"></country></address><ref type="url">http://www.inria.fr/equipes/parole</ref></desc><listRelation><relation active="#struct-160" type="direct"></relation><relation name="UMR7503" active="#struct-441569" type="indirect"></relation><relation active="#struct-300009" type="indirect"></relation><relation active="#struct-300291" type="indirect"></relation><relation active="#struct-300292" type="indirect"></relation><relation active="#struct-300293" type="indirect"></relation><relation active="#struct-2496" type="direct"></relation></listRelation><tutelles><tutelle active="#struct-160" type="direct"><org type="laboratory" xml:id="struct-160" status="OLD"><orgName>Laboratoire Lorrain de Recherche en Informatique et ses Applications</orgName><orgName type="acronym">LORIA</orgName><desc><address><addrLine>Campus Scientifique BP 239 54506 Vandoeuvre-lès-Nancy Cedex</addrLine><country key="FR"></country></address><ref type="url">http://www.loria.fr</ref></desc><listRelation><relation name="UMR7503" active="#struct-441569" type="direct"></relation><relation active="#struct-300009" type="direct"></relation><relation active="#struct-300291" type="direct"></relation><relation active="#struct-300292" type="direct"></relation><relation active="#struct-300293" type="direct"></relation></listRelation></org></tutelle><tutelle name="UMR7503" active="#struct-441569" type="indirect"><org type="institution" xml:id="struct-441569" status="VALID"><idno type="ISNI">0000000122597504</idno><idno type="IdRef">02636817X</idno><orgName>Centre National de la Recherche Scientifique</orgName><orgName type="acronym">CNRS</orgName><date type="start">1939-10-19</date><desc><address><country key="FR"></country></address><ref type="url">http://www.cnrs.fr/</ref></desc></org></tutelle><tutelle active="#struct-300009" type="indirect"><org type="institution" xml:id="struct-300009" status="VALID"><orgName>Institut National de Recherche en Informatique et en Automatique</orgName><orgName type="acronym">Inria</orgName><desc><address><addrLine>Domaine de VoluceauRocquencourt - BP 10578153 Le Chesnay Cedex</addrLine><country key="FR"></country></address><ref type="url">http://www.inria.fr/en/</ref></desc></org></tutelle><tutelle active="#struct-300291" type="indirect"><org type="institution" xml:id="struct-300291" status="OLD"><orgName>Université Henri Poincaré - Nancy 1</orgName><orgName type="acronym">UHP</orgName><date type="end">2011-12-31</date><desc><address><addrLine>24-30 rue Lionnois, BP 60120, 54 003 NANCY cedex, France</addrLine><country key="FR"></country></address></desc></org></tutelle><tutelle active="#struct-300292" type="indirect"><org type="institution" xml:id="struct-300292" status="OLD"><orgName>Université Nancy 2</orgName><date type="end">2011-12-31</date><desc><address><addrLine>91 avenue de la Libération, BP 454, 54001 Nancy cedex</addrLine><country key="FR"></country></address></desc></org></tutelle><tutelle active="#struct-300293" type="indirect"><org type="institution" xml:id="struct-300293" status="OLD"><orgName>Institut National Polytechnique de Lorraine</orgName><orgName type="acronym">INPL</orgName><date type="end">2011-12-31</date><desc><address><country key="FR"></country></address></desc></org></tutelle><tutelle active="#struct-2496" type="direct"><org type="laboratory" xml:id="struct-2496" status="OLD"><orgName>INRIA Lorraine</orgName><desc><address><addrLine>615 rue du Jardin Botanique 54600 Villers-lès-Nancy</addrLine><country key="FR"></country></address><ref type="url">http://www.inria.fr/centre-de-recherche-inria/nancy-grand-est</ref></desc><listRelation><relation active="#struct-300009" type="direct"></relation></listRelation></org></tutelle></tutelles></hal:affiliation><country>France</country><placeName><settlement type="city">Nancy</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Université Nancy 2</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName><placeName><settlement type="city">Nancy</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Institut national polytechnique de Lorraine</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>LVCSR</term><term>OOV</term><term>proper names</term><term>topic models</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Retrieving Proper Names (PNs) relevant to an audio documentcan improve speech recognition and content based audio-videoindexing. Latent Dirichlet Allocation (LDA) topic model hasbeen used to retrieve Out-Of-Vocabulary (OOV) PNs relevant toan audio document with good recall rates. However, retrieval ofOOV PNs using LDA is affected by two issues, which we studyin this paper: (1)Word Frequency Bias (less frequent OOV PNsare ranked lower); (2) Loss of Specificity (the reduced topicspace representation loses lexical context). Entity-Topic modelshave been proposed as extensions of LDA to specifically learnrelations between words, entities (PNs) and topics. We studyOOV PN retrieval with Entity-Topic models and show that theyare also affected by word frequency bias and loss of specificity.We evaluate our proposed methods for rare OOV PN re-rankingand lexical context re-ranking for LDA as well as for Entity-Topic models. The results show an improvement in both Recalland the Mean Average Precision.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Metz</li><li>Nancy</li></settlement><orgName><li>Institut national polytechnique de Lorraine</li><li>Université Nancy 2</li><li>Université de Lorraine</li></orgName></list><tree><country name="France"><region name="Grand Est"><name sortKey="Sheikh, Imran" sort="Sheikh, Imran" uniqKey="Sheikh I" first="Imran" last="Sheikh">Imran Sheikh</name></region><name sortKey="Fohr, Dominique" sort="Fohr, Dominique" uniqKey="Fohr D" first="Dominique" last="Fohr">Dominique Fohr</name><name sortKey="Illina, Irina" sort="Illina, Irina" uniqKey="Illina I" first="Irina" last="Illina">Irina Illina</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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