Summary of the paper
| Title | Locating Requests among Open Source Software Communication Messages |
|---|---|
| Authors | Ioannis Korkontzelos and Sophia Ananiadou |
| Abstract | As a first step towards assessing the quality of support offered online for Open Source Software (OSS), we address the task of locating requests, i.e., messages that raise an issue to be addressed by the OSS community, as opposed to any other message. We present a corpus of online communication messages randomly sampled from newsgroups and bug trackers, manually annotated as requests or non-requests. We identify several linguistically shallow, content-based heuristics that correlate with the classification and investigate the extent to which they can serve as independent classification criteria. Then, we train machine-learning classifiers on these heuristics. We experiment with a wide range of settings, such as different learners, excluding some heuristics and adding unigram features of various parts-of-speech and frequency. We conclude that some heuristics can perform well, while their accuracy can be improved further using machine learning, at the cost of obtaining manual annotations. |
| Topics | Document Classification, Text categorisation, Text Mining |
Full paper  |
Locating Requests among Open Source Software Communication Messages |
| Bibtex | @InProceedings{KORKONTZELOS14.498, author = {Ioannis Korkontzelos and Sophia Ananiadou}, title = {Locating Requests among Open Source Software Communication Messages}, booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)}, year = {2014}, month = {may}, date = {26-31}, address = {Reykjavik, Iceland}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-8-4}, language = {english} } |