This page contains additional resources related to the manuscript "Discovering Information Explaining API Types Using text Classification" submitted to ICSE 2015 by Petrosyan, Robillard, De Mori

To improve part-of-speech (POS) tags assigned by Stanford Parser in case of technical concepts we reimplemented a multi-word term detection algorithm and ran it on Official Java Tutorials. Afterwards we chose top phrases and forced POS tagger to tag them as nouns.
List of Multi-word phrases: multi_word_phrases.pdf

For out classification task we used Dependency-based Features. To find useful dependencies we used the Java tutorials to extracted 1785 dependencies in which either the governor or the dependent was a code-like term(CLT). After manual annotation we calculated a z-score and normalized it to use as a weight for the dependency. The useful dependencies instances overall mapped to 243 distinct typed dependencies and 39 distinct relations.
List of useful dependencies used: dependencies.csv
List of useful relation types used: relations.csv

For constructing training set for the classification task we needed to manually annotate the tutorials. To ensure a high level of rigour in our annotation process, we constructed a detailed annotation guide.
Annotation Guide: annotation_guide.pdf

Studying how to discover tutorial sections relevant to API types requires a corpus of tutorials. We selected five tutorials covering four different Java APIs. Here are those five tutorials after pre-processing and annotation.
Annotated tutorials: JodaTime Math Col. Official Col. Jenkov Smack