Unsupervised language model adaptation using LDA-based mixture models and latent semantic marginals

Md. Akmal Haidar; Douglas O’Shaughnessy

doi:10.1016/j.csl.2014.06.002

ScienceGate Book Chapters

JOURNAL ARTICLE

Unsupervised language model adaptation using LDA-based mixture models and latent semantic marginals

Md. Akmal Haidar Douglas O’Shaughnessy

Year: 2014 Journal: Computer Speech & Language Vol: 29 (1)Pages: 20-31 Publisher: Elsevier BV

DOI: 10.1016/j.csl.2014.06.002

Get Full-Text PDF Get Analytical Report

Keywords:

Computer science Latent Dirichlet allocation Artificial intelligence Language model Topic model Probabilistic latent semantic analysis Pattern recognition (psychology) Scaling Mixture model Cluster analysis Machine learning Mathematics

Metrics

Cited By

2.90

FWCI (Field Weighted Citation Impact)

Refs

0.91

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Speech Recognition and Synthesis

Physical Sciences → Computer Science → Artificial Intelligence

Topic Modeling

Physical Sciences → Computer Science → Artificial Intelligence

Natural Language Processing Techniques

Physical Sciences → Computer Science → Artificial Intelligence

Unsupervised language model adaptation using LDA-based mixture models and latent semantic marginals

Metrics

Citation History

Topics

Related Documents

Unsupervised Language Model Adaptation Using Latent Semantic Marginals

Unsupervised language model adaptation using latent semantic marginals

Lda-Based Lm Adaptation Using Latent Semantic Marginals And Minimum Discriminant Information

Unsupervised Language Model Adaptation Using Latent Dirichlet Allocation And Dynamic Marginals

Integrating MAP, marginals, and unsupervised language model adaptation