Publication Type:

Conference Paper

Source:

2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA), IEEE, Coimbatore, India, India (2019)

ISBN:

9781728101675

URL:

https://ieeexplore.ieee.org/abstract/document/8822023

Keywords:

Analytical models, Autism, correlated topic modeling, Correlation, Data mining, document handling, dynamic topic modeling, Heuristic algorithms, Knowledge discovery, log likelihood, Logistics, perplexity, QA dataset, question answer dataset, question answering (information retrieval), Real-time systems, Topic Modeling

Abstract:

Topic modeling is a set of algorithms which is used to mine the data that is hidden inside a large collection of documents. In this paper we discusses about the Correlated topic modeling and dynamic topic modeling in detail and comparing their performance on a question answer dataset based on autism. Log Likelihood and Perplexity are the measures used for comparing the discussed topic modeling algorithms.

Cite this Research Publication

B. K. R., Krishna, G. H. G., and Parameswaran, L., “A Performance Evaluation of Correlated and Dynamic Topic Modeling on a QA Dataset”, in 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, India, 2019.