Improving Statistical Language Models by Removing Impossible Events
Identifieur interne : 009644 ( Main/Exploration ); précédent : 009643; suivant : 009645Improving Statistical Language Models by Removing Impossible Events
Auteurs : Armelle Brun [France] ; David Langlois [France] ; Kamel Smaïli [France] ; Jean-Paul Haton [France]Source :
Descripteurs français
- mix :
Abstract
This paper deals with a new method which detects impossible bigrams in a space of $V^2$ bigrams designed from a vocabulary V and which discards them from a statistical language model. We claim that discarding ungrammatical events, which are impossible in a well written text, will improve language models and as well, reduce the complexity of search algorithms in speech recognition. The Purged Language Model (PLM) needs a set of impossible bigrams, which are detected by using automatic rules based on a class model, phonology rules, etc. Methods for redistributing the sum of probabilities issued from impossible bigrams among possible events have been developped. This idea allows us to take advantage of natural language constraints and to include linguistic criteria in statistical language models. The PLM has been tested on a test corpus of 2M words and achieves a perplexity improvement of 51% under certain conditions.
Url:
Affiliations:
- France
- Grand Est, Lorraine (région)
- Nancy
- Institut national polytechnique de Lorraine, Université Nancy 2, Université de Lorraine
Links toward previous steps (curation, corpus...)
- to stream Hal, to step Corpus: 002A23
- to stream Hal, to step Curation: 002A23
- to stream Hal, to step Checkpoint: 005E16
- to stream Main, to step Merge: 009C25
- to stream Main, to step Curation: 009644
Le document en format XML
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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="fr"><term>impossible events</term><term>modèle de langage statistique</term><term>statistical language modelling</term><term>événements impossibles</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">This paper deals with a new method which detects impossible bigrams in a space of $V^2$ bigrams designed from a vocabulary V and which discards them from a statistical language model. We claim that discarding ungrammatical events, which are impossible in a well written text, will improve language models and as well, reduce the complexity of search algorithms in speech recognition. The Purged Language Model (PLM) needs a set of impossible bigrams, which are detected by using automatic rules based on a class model, phonology rules, etc. Methods for redistributing the sum of probabilities issued from impossible bigrams among possible events have been developped. This idea allows us to take advantage of natural language constraints and to include linguistic criteria in statistical language models. The PLM has been tested on a test corpus of 2M words and achieves a perplexity improvement of 51% under certain conditions.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><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="Brun, Armelle" sort="Brun, Armelle" uniqKey="Brun A" first="Armelle" last="Brun">Armelle Brun</name></region><name sortKey="Haton, Jean Paul" sort="Haton, Jean Paul" uniqKey="Haton J" first="Jean-Paul" last="Haton">Jean-Paul Haton</name><name sortKey="Langlois, David" sort="Langlois, David" uniqKey="Langlois D" first="David" last="Langlois">David Langlois</name><name sortKey="Smaili, Kamel" sort="Smaili, Kamel" uniqKey="Smaili K" first="Kamel" last="Smaïli">Kamel Smaïli</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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