The Journal of Machine Learning Research
The author-topic model for authors and documents
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Group and topic discovery from relations and text
Proceedings of the 3rd international workshop on Link discovery
SOA Principles of Service Design (The Prentice Hall Service-Oriented Computing Series from Thomas Erl)
A Faceted Classification Based Approach to Search and Rank Web APIs
ICWS '08 Proceedings of the 2008 IEEE International Conference on Web Services
hRESTS: An HTML Microformat for Describing RESTful Web Services
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Unified publication and discovery of semantic Web services
ACM Transactions on the Web (TWEB)
Topic and role discovery in social networks
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Joint sentiment/topic model for sentiment analysis
Proceedings of the 18th ACM conference on Information and knowledge management
Web Service Search on Large Scale
ICSOC-ServiceWave '09 Proceedings of the 7th International Joint Conference on Service-Oriented Computing
Labeled LDA: a supervised topic model for credit attribution in multi-labeled corpora
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Investigating Web APIs on the World Wide Web
ECOWS '10 Proceedings of the 2010 Eighth IEEE European Conference on Web Services
Handbook of Semantic Web Technologies
Handbook of Semantic Web Technologies
Partially labeled topic models for interpretable text mining
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Weakly Supervised Joint Sentiment-Topic Detection from Text
IEEE Transactions on Knowledge and Data Engineering
Automated information extraction from web APIs documentation
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
Why people hate your app: making sense of user feedback in a mobile app store
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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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.