PICF-LDA: a topic enhanced LDA with probability incremental correction factor for Web API service clustering

WT-LDA: User Tagging Augmented LDA for Web Service Clustering

Clustering Web services that groups together services with similar functionalities helps improve both the accuracy and efficiency of the Web service search engines. An important limitation of existing Web service clustering approaches is that they solely focus on utilizing WSDL (Web Service Description Language) documents. There has been a recent trend of using user-contributed tagging data to ...

Feature LDA: A Supervised Topic Model for Automatic Detection of Web API Documentations from the Web

Web APIs have gained increasing popularity in recent Web service technology development owing to its simplicity of technology stack and the proliferation of mashups. However, efficiently discovering Web APIs and the relevant documentations on the Web is still a challenging task even with the best resources available on the Web. In this paper we cast the problem of detecting the Web API document...

Topic Models - LDA - Experiments

Web Event Topic Analysis by Topic Feature Clustering and Extended LDA Model

To analyze topics of a large number of web events, we proposed an event topic analysis approach by topic feature clustering and extended LDA (latent dirichlet allocation) model. The extended LDA model is dimension LDA (DLDA) which integrates topic probability of LDA model. We represent an event as a multi-dimensions vector and use DLDA model to select topic feature words in events. We aggregate...

Topic Modeling using LDA with Feedback Mechanisms

Topic models provide a way to identify the latent topics from a collection of documents. Although the identified topics often appear quite representative of the data; just as often, there are parts of the output that appear erroneous or otherwise difficult to interpret by humans. This is a limitation of topic models that can be remedied by user feedback mechanisms. In this paper, I discuss two ...