| 36021 |
Creator |
20fbc73bc6c0afc98372cf072a54acc4 |
| 36021 |
Creator |
2482a533b100c51b082644502f2b86e0 |
| 36021 |
Creator |
686a5c485be2b64d259e575fed2f711d |
| 36021 |
Creator |
cdd7ce296512d3575bcad552e19f8995 |
| 36021 |
Date |
2012 |
| 36021 |
Is Part Of |
repository |
| 36021 |
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. |
| 36021 |
authorList |
authors |
| 36021 |
presentedAt |
ext-76fa5196e3eb976fdb43c85693c20ad7 |
| 36021 |
status |
peerReviewed |
| 36021 |
uri |
http://data.open.ac.uk/oro/document/113637 |
| 36021 |
uri |
http://data.open.ac.uk/oro/document/113652 |
| 36021 |
uri |
http://data.open.ac.uk/oro/document/113660 |
| 36021 |
uri |
http://data.open.ac.uk/oro/document/113661 |
| 36021 |
uri |
http://data.open.ac.uk/oro/document/113662 |
| 36021 |
uri |
http://data.open.ac.uk/oro/document/113663 |
| 36021 |
uri |
http://data.open.ac.uk/oro/document/122765 |
| 36021 |
type |
AcademicArticle |
| 36021 |
type |
Article |
| 36021 |
label |
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 Nov
2012, Boston, MA, USA, pp. 328–343. |
| 36021 |
label |
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 Nov
2012, Boston, MA, USA, pp. 328–343. |
| 36021 |
Title |
Feature LDA: a supervised topic model for automatic detection of Web API documentations
from the Web |
| 36021 |
in dataset |
oro |