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Topic models are also referred as probabilistic topic models, which refers to statistic algorithms for discovering the latent semantic structures of a an extensive text body. In the age of information, the amount of the written material we encounter each day is simply beyond our processing capacity. Topic models can help to organize and offer insights for us to understand large collections of unstructured text bodies. Originally developed as a text-mining tool, topic models now has been used to detect instructive structures in data such genetic information, images and networks.
I am doing more editing here because my class requires me to do so. This editing seems to be able to make my profile looks better. Therefore, I am typing. My plan for the next month to come is that I will study hard for my Bar exam, and then fly back home to take the Bar, then come back to the US to study more for LSAT. [1]
- ^ Blei, David (April 2012). "Probabilistic Topic Models". Communications of the ACM. 55 (4): 77-84.
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