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ABSTRACT
Use of document genre in information retrieval systems has the potential to improve the task-appropriateness of results. However, genre classification remains a challenging problem. We describe a case study of genre classification in a software engineering workplace domain, which includes the development of a genre taxonomy and experiments in automatic genre classification using supervised machine learning. We present results based on evaluation using real-life enterprise data from this work domain.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
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[doi> 10.1145/775152.775204]
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