TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Merten, Thorsten
A1  - Mager, Bastian
A1  - Bürsner, Simone
A1  - Paech, Barbara
T1  - Classifying Unstructured Data into Natural Language Text and Technical Information
T2  - MSR 2014. Proceedings of the 11th Working Conference on Mining Software Repositories. May 31 - June 1, 2014, Hyderabad, India
N2  - Software repository data, for example in issue tracking systems, include natural language text and technical information, which includes anything from log files via code snippets to stack traces. However, data mining is often only interested in one of the two types e.g. in natural language text when looking at text mining. Regardless of which type is being investigated, any techniques used have to deal with noise caused by fragments of the other type i.e. methods interested in natural language have to deal with technical fragments and vice versa. This paper proposes an approach to classify unstructured data, e.g. development documents, into natural language text and technical information using a mixture of text heuristics and agglomerative hierarchical clustering. The approach was evaluated using 225 manually annotated text passages from developer emails and issue tracker data. Using white space tokenization as a basis, the overall precision of the approach is 0.84 and the recall is 0.85.
KW  - Mining software repositories
KW  - unstructured data
KW  - preprocessing
KW  - heuristics
KW  - hierarchical clustering
SN  - 978-1-4503-2863-0
SB  - 978-1-4503-2863-0
U6  - https://doi.org/10.1145/2597073.2597112
DO  - https://doi.org/10.1145/2597073.2597112
SP  - 300
EP  - 303
PB  - Association for Computing Machinery
CY  - New York, NY, United States
ER  -