InforLorV4, Hal, Corpus, bibRecord, 004875

Study of Entity-Topic Models for OOV Proper Name Retrieval

Identifieur interne : 004875 ( Hal/Corpus ); précédent : 004874; suivant : 004876

Study of Entity-Topic Models for OOV Proper Name Retrieval

Auteurs : Imran Sheikh ; Irina Illina ; Dominique Fohr

Source :

RBID : Hal:hal-01184955

English descriptors

mix :
- LVCSR, OOV, proper names, topic models.

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:

https://hal.archives-ouvertes.fr/hal-01184955

Links to Exploration step

Hal:hal-01184955

Le document en format XML

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<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>
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<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.</abstract>
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Study of Entity-Topic Models for OOV Proper Name Retrieval

Study of Entity-Topic Models for OOV Proper Name Retrieval

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