Topic Significance Ranking of LDA Generative Models
published: Oct. 20, 2009, recorded: September 2009, views: 582
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Topic models, like Latent Dirichlet Allocation (LDA), have been recently used to automatically generate text corpora topics, and to subdivide the corpus words among those topics. However, not all the estimated topics are of equal importance or correspond to genuine themes of the domain. Some of the topics can be a collection of irrelevant words, or represent insignificant themes. Current approaches to topic modeling perform manual examination to find meaningful topics. This paper presents the first automated unsupervised analysis of LDA models to identify junk topics from legitimate ones, and to rank the topic significance. Basically, the distance between a topic distribution and three definitions of “junk distributions” is computed using a variety of measures, from which an expressive figure of the topic significance is implemented using 4-phase Weighted Combination approach. Our experiments on synthetic and benchmark datasets show the effectiveness of the proposed approach in ranking the topic significance.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !