De-identification in natural language processing
Abstract: Natural language processing (NLP) systems usually require a huge amount of textual data but the publication of such datasets is often hindered by privacy and data protection issues. Here, we discuss the questions of de-identification related to three NLP areas, namely, clinical NLP, NLP for social media and information extraction from resumes. We also illustrate how de-identification is related to named entity recognition and we argue that de-identification tools can be successfully built on named entity recognizers.
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