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Feature LDA: a supervised topic model for automatic detection of Web API documentations from the Web

Lin, Chenghua; He, Yulan; Pedrinaci, Carlos and Domingue, John (2012). Feature LDA: a supervised topic model for automatic detection of Web API documentations from the Web. In: The 11th International Semantic Web Conference (ISWC 2012), 11-15 November 2012, Boston, MA, USA, pp. 328–343.

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URL: http://iswc2012.semanticweb.org/
DOI (Digital Object Identifier) Link: https://doi.org/10.1007/978-3-642-35176-1_21
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Abstract

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.

Item Type: Conference or Workshop Item
Copyright Holders: 2012 The Authors
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
Related URLs:
Item ID: 36021
Depositing User: Kay Dave
Date Deposited: 17 Jan 2013 09:40
Last Modified: 06 Oct 2016 02:55
URI: http://oro.open.ac.uk/id/eprint/36021
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