Date of Publication :25th April 2018
Abstract: An information processing unit takes an input of raw data, this data is passed through various steps to generate useful information. Topic modeling is a process in which raw text corpus is passed onto a series of steps so that the document can be categorized into a set of topics.There are various methods for achieving topic modelling like Tf-Idf, LSI,LDA. These topics are then looked up onto global repository of linked information such as dbpedia. This paper explores ways to map local topics with global counterparts and retrieve useful information so as to develop intelligent systems capable of understanding semantics.
Reference :
-
- B. V. Barde and A. M. Bainwad, ―An overview of topic mod-eling methods and tools,‖ 2017 International Conference on Intelligent Computing and Control Systems (ICICCS), 2017
- Blei, David M.; Ng, Andrew Y.; Jordan, Michael I (January 2003). Lafferty, John, ed. "Latent Dirichlet Allocation". Journal of Machine Learning Research
- Pattern-based topics for document modelling in information filtering,Gao Y, Xu Y, Li Y, IEEE Transactions on Knowledge and Data Engineering, vol. 27, issue 6 (2015) pp. 1629-1642 Published by IEEE Computer Society
- Mishra, Apra, and Santosh Vishwakarma. ―Analysis of TF-IDF Model and Its Variant for Document Retrieval.‖ 2015 In-ternational Conference on Computational Intelligence and Communication Networks (CICN), 2015, doi:10.1109/cicn.2015.157.