PGLDA: enhancing the precision of topic modelling using poisson gamma (PG) and latent dirichlet allocation (LDA) for text information retrieval
The Poisson document length distribution has been used extensively in the past for modeling topics with the expectation that its effect will disintegrate at the end of the model definition. This procedure often leads to down Playing word correlation with topics and reducing retrieved documents�...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English English English |
| Published: |
2021
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/4890/1/24p%20IBRAHIM%20BALA%20BAKARI.pdf http://eprints.uthm.edu.my/4890/2/IBRAHIM%20BALA%20BAKARI%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/4890/3/IBRAHIM%20BALA%20BAKARI%20WATERMARK.pdf |
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| Summary: | The Poisson document length distribution has been used extensively in the past for
modeling topics with the expectation that its effect will disintegrate at the end of the
model definition. This procedure often leads to down Playing word correlation with
topics and reducing retrieved documents' precision or accuracy. The existing
document model, such as the Latent Dirichlet Allocation (LDA) model, does not
accommodate words' semantic representation. Therefore, in this thesis, the PoissonGamma
Latent Dirichlet Allocation (PGLDA) model for modeling word
dependencies in topic modeling is introduced. The PGLDA model relaxes the words
independence assumption in the existing Latent Dirichlet Allocation (LDA) model
by introducing the Gamma distribution that captures the correlation between adjacent
words in documents. The PGLDA is hybridized with the distributed representation of
documents (Doc2Vec) and topics (Topic2Vec) to form a new model named
PGLDA2Vec. The hybridization process was achieved by averaging the Doc2Vec
and Topic2Vec vectors to form new word representation vectors, combined with
topics with the largest estimated probability using PGLDA. Model estimations for
PGLDA and PGLDA2Vec models were achieved by combining the Laplacian
approximation of log-likelihood for PGLDA and Feed-Forward Neural Network
(FFN) approaches of Doc2Vec and Topic2Vec. The proposed PGLDA and the
hybrid PGLDA2Vec models were assessed using precision, micro F1 scores,
perplexity, and coherence score. The empirical analysis results using three real-world
datasets (20 Newsgroups, AG'News, and Reuters) showed that the hybrid
PGLDA2Vec model with an average precision of 86.6%, and an average F1 score of
96.3%, across the three datasets is better than other competing models reviewed. |
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