A new method based on context for combining statistical language models
Identifieur interne :
000938 ( PascalFrancis/Corpus );
précédent :
000937;
suivant :
000939
A new method based on context for combining statistical language models
Auteurs : David Langlois ;
Kamel Smaïli ;
Jean-Paul HatonSource :
-
Lecture notes in computer science [ 0302-9743 ] ; 2001.
RBID : Pascal:01-0381622
Descripteurs français
English descriptors
Abstract
In this paper we propose a new method to extract from a corpus the histories for which a given language model is better than another one. The decision is based on a measure stemmed from perplexity. This measure allows, for a given history, to compare two language models, and then to choose the best one for this history. Using this principle, and with a 20K vocabulary words, we combined two language models: a bigram and a distant bigram. The contribution of a distant bigram is significant and outperforms a bigram model by 7.5%. Moreover, the performance in Shannon game are improved. We show through this article that we proposed a cheaper framework in comparison to the maximum entropy principle, for combining language models. In addition, the selected histories for which a model is better than another one, have been collected and studied. Almost, all of them are beginnings of very frequently used French phrases. Finally, by using this principle, we achieve a better trigram model in terms of parameters and perplexity. This model is a combination of a bigram and a trigram based on a selected history.
Notice en format standard (ISO 2709)
Pour connaître la documentation sur le format Inist Standard.
| pA |
| A01 | 01 | 1 | | @0 0302-9743 |
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| A05 | | | | @2 2116 |
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| A08 | 01 | 1 | ENG | @1 A new method based on context for combining statistical language models |
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| A09 | 01 | 1 | ENG | @1 CONTEXT 2001 : modeling and using context : Dundee, 27-30 July 2001 |
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| A11 | 01 | 1 | | @1 LANGLOIS (David) |
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| A11 | 02 | 1 | | @1 SMAÏLI (Kamel) |
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| A11 | 03 | 1 | | @1 HATON (Jean-Paul) |
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| A12 | 01 | 1 | | @1 AKMAN (Varol) @9 ed. |
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| A12 | 02 | 1 | | @1 BOUQUET (Paolo) @9 ed. |
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| A12 | 03 | 1 | | @1 THOMASON (Richmond) @9 ed. |
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| A12 | 04 | 1 | | @1 YOUNG (Roger A.) @9 ed. |
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| A14 | 01 | | | @1 LORIA Laboratory, Campus Scientifique BP 239 @2 54506 Vandœuvre-Lès-Nancy @3 FRA @Z 1 aut. @Z 2 aut. @Z 3 aut. |
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| A20 | | | | @1 235-247 |
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| A21 | | | | @1 2001 |
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| A23 | 01 | | | @0 ENG |
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| A26 | 01 | | | @0 3-540-42379-6 |
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| A43 | 01 | | | @1 INIST @2 16343 @5 354000092486680180 |
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| A44 | | | | @0 0000 @1 © 2001 INIST-CNRS. All rights reserved. |
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| A45 | | | | @0 15 ref. |
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| A47 | 01 | 1 | | @0 01-0381622 |
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| A60 | | | | @1 P @2 C |
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| A61 | | | | @0 A |
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| A66 | 01 | | | @0 DEU |
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| A66 | 02 | | | @0 USA |
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| C01 | 01 | | ENG | @0 In this paper we propose a new method to extract from a corpus the histories for which a given language model is better than another one. The decision is based on a measure stemmed from perplexity. This measure allows, for a given history, to compare two language models, and then to choose the best one for this history. Using this principle, and with a 20K vocabulary words, we combined two language models: a bigram and a distant bigram. The contribution of a distant bigram is significant and outperforms a bigram model by 7.5%. Moreover, the performance in Shannon game are improved. We show through this article that we proposed a cheaper framework in comparison to the maximum entropy principle, for combining language models. In addition, the selected histories for which a model is better than another one, have been collected and studied. Almost, all of them are beginnings of very frequently used French phrases. Finally, by using this principle, we achieve a better trigram model in terms of parameters and perplexity. This model is a combination of a bigram and a trigram based on a selected history. |
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| C02 | 01 | X | | @0 001D02C04 |
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| C03 | 01 | X | FRE | @0 Méthode entropie maximum @5 01 |
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| C03 | 01 | X | ENG | @0 Method of maximum entropy @5 01 |
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| C03 | 01 | X | SPA | @0 Método entropía máxima @5 01 |
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| C03 | 02 | X | FRE | @0 Principe maximum @5 02 |
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| C03 | 02 | X | ENG | @0 Maximum principle @5 02 |
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| C03 | 02 | X | SPA | @0 Principio máximo @5 02 |
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| C03 | 03 | X | FRE | @0 Théorie Shannon @5 03 |
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| C03 | 03 | X | ENG | @0 Shannon theory @5 03 |
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| C03 | 03 | X | SPA | @0 Teoría Shannon @5 03 |
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| C03 | 04 | X | FRE | @0 Vocabulaire @5 04 |
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| C03 | 04 | X | ENG | @0 Vocabulary @5 04 |
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| C03 | 04 | X | SPA | @0 Vocabulario @5 04 |
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| C03 | 05 | X | FRE | @0 Mot @5 05 |
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| C03 | 05 | X | ENG | @0 Word @5 05 |
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| C03 | 05 | X | SPA | @0 Palabra @5 05 |
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| C03 | 06 | X | FRE | @0 Evaluation performance @5 06 |
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| C03 | 06 | X | ENG | @0 Performance evaluation @5 06 |
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| C03 | 06 | X | SPA | @0 Evaluación prestación @5 06 |
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| C03 | 07 | X | FRE | @0 Modélisation @5 07 |
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| C03 | 07 | X | ENG | @0 Modeling @5 07 |
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| C03 | 07 | X | SPA | @0 Modelización @5 07 |
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| C03 | 08 | X | FRE | @0 Méthode statistique @5 08 |
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| C03 | 08 | X | ENG | @0 Statistical method @5 08 |
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| C03 | 08 | X | SPA | @0 Método estadístico @5 08 |
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| C03 | 09 | X | FRE | @0 Reconnaissance parole @5 09 |
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| C03 | 09 | X | ENG | @0 Speech recognition @5 09 |
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| C03 | 09 | X | SPA | @0 Reconocimiento palabra @5 09 |
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| N21 | | | | @1 267 |
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|
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| pR |
| A30 | 01 | 1 | ENG | @1 Modeling and using context. International and interdisciplinary conference @2 3 @3 Dundee GBR @4 2001-07-27 |
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|
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Format Inist (serveur)
| NO : | PASCAL 01-0381622 INIST |
|---|
| ET : | A new method based on context for combining statistical language models |
|---|
| AU : | LANGLOIS (David); SMAÏLI (Kamel); HATON (Jean-Paul); AKMAN (Varol); BOUQUET (Paolo); THOMASON (Richmond); YOUNG (Roger A.) |
|---|
| AF : | LORIA Laboratory, Campus Scientifique BP 239/54506 Vandœuvre-Lès-Nancy/France (1 aut., 2 aut., 3 aut.) |
|---|
| DT : | Publication en série; Congrès; Niveau analytique |
|---|
| SO : | Lecture notes in computer science; ISSN 0302-9743; Allemagne; Da. 2001; Vol. 2116; Pp. 235-247; Bibl. 15 ref. |
|---|
| LA : | Anglais |
|---|
| EA : | In this paper we propose a new method to extract from a corpus the histories for which a given language model is better than another one. The decision is based on a measure stemmed from perplexity. This measure allows, for a given history, to compare two language models, and then to choose the best one for this history. Using this principle, and with a 20K vocabulary words, we combined two language models: a bigram and a distant bigram. The contribution of a distant bigram is significant and outperforms a bigram model by 7.5%. Moreover, the performance in Shannon game are improved. We show through this article that we proposed a cheaper framework in comparison to the maximum entropy principle, for combining language models. In addition, the selected histories for which a model is better than another one, have been collected and studied. Almost, all of them are beginnings of very frequently used French phrases. Finally, by using this principle, we achieve a better trigram model in terms of parameters and perplexity. This model is a combination of a bigram and a trigram based on a selected history. |
|---|
| CC : | 001D02C04 |
|---|
| FD : | Méthode entropie maximum; Principe maximum; Théorie Shannon; Vocabulaire; Mot; Evaluation performance; Modélisation; Méthode statistique; Reconnaissance parole |
|---|
| ED : | Method of maximum entropy; Maximum principle; Shannon theory; Vocabulary; Word; Performance evaluation; Modeling; Statistical method; Speech recognition |
|---|
| SD : | Método entropía máxima; Principio máximo; Teoría Shannon; Vocabulario; Palabra; Evaluación prestación; Modelización; Método estadístico; Reconocimiento palabra |
|---|
| LO : | INIST-16343.354000092486680180 |
|---|
| ID : | 01-0381622 |
|---|
Links to Exploration step
Pascal:01-0381622
Le document en format XML
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<ET>A new method based on context for combining statistical language models</ET>
<AU>LANGLOIS (David); SMAÏLI (Kamel); HATON (Jean-Paul); AKMAN (Varol); BOUQUET (Paolo); THOMASON (Richmond); YOUNG (Roger A.)</AU>
<AF>LORIA Laboratory, Campus Scientifique BP 239/54506 Vandœuvre-Lès-Nancy/France (1 aut., 2 aut., 3 aut.)</AF>
<DT>Publication en série; Congrès; Niveau analytique</DT>
<SO>Lecture notes in computer science; ISSN 0302-9743; Allemagne; Da. 2001; Vol. 2116; Pp. 235-247; Bibl. 15 ref.</SO>
<LA>Anglais</LA>
<EA>In this paper we propose a new method to extract from a corpus the histories for which a given language model is better than another one. The decision is based on a measure stemmed from perplexity. This measure allows, for a given history, to compare two language models, and then to choose the best one for this history. Using this principle, and with a 20K vocabulary words, we combined two language models: a bigram and a distant bigram. The contribution of a distant bigram is significant and outperforms a bigram model by 7.5%. Moreover, the performance in Shannon game are improved. We show through this article that we proposed a cheaper framework in comparison to the maximum entropy principle, for combining language models. In addition, the selected histories for which a model is better than another one, have been collected and studied. Almost, all of them are beginnings of very frequently used French phrases. Finally, by using this principle, we achieve a better trigram model in terms of parameters and perplexity. This model is a combination of a bigram and a trigram based on a selected history.</EA>
<CC>001D02C04</CC>
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