Feature LDA: A supervised topic model for automatic detection of web API documentations from the web

Chenghua Lin, Yulan He, Carlos Pedrinaci, John Domingue

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

15 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationThe Semantic Web - ISWC 2012
Subtitle of host publication11th International Semantic Web Conference Boston, MA, USA, November 11-15, 2012 Proceedings, Part I
Place of PublicationBerlin
Number of pages16
ISBN (Print)9783642351754
Publication statusPublished - 2012

Publication series

NameLecture Notes in Computer Science


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