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 documentations as a text classification problem of classifying a given Web page as Web API associated or not. We propose a supervised generative topic model called feature latent Dirichlet allocation (feaLDA) which offers a generic probabilistic framework for automatic detection of Web APIs. feaLDA not only captures the correspondence between data and the associated class labels, but also provides a mechanism for incorporating side information such as labelled features automatically learned from data that can effectively help improving classification performance. Extensive experiments on our Web APIs documentation dataset shows that the feaLDA model outperforms three strong supervised baselines including naive Bayes, support vector machines, and the maximum entropy model, by over 3% in classification accuracy. In addition, feaLDA also gives superior performance when compared against other existing supervised topic models.

Use of Google as a tool to aid diagnosis by optometry students

Background: Previous work has shown that medical problems can be diagnosed by practitioners using Google. The aim of this study was to determine whether optometry students would benefit from using Google when diagnosing eye diseases. Methods: Participants were given symptoms and signs and instructed to list three key words and use them to search Aston University e-Library and Google UK. Results: Aston University e-Library only search resulted in correct diagnosis in 16 of 60 simulated cases. Aston e-Library plus Google search resulted in correct diagnosis in 31 of 60 simulated cases. Conclusion: Google is a useful aid to help optometry students improve their success rate when diagnosing eye conditions.