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There is an increasing interest in studying natural language and computercode together, as large corpora of programming texts become readily availableon the Internet. For example, StackOverflow currently has over 15 millionprogramming related questions written by 8.5 million users. Meanwhile, there isstill a lack of fundamental NLP techniques for identifying code tokens orsoftware-related named entities that appear within natural language sentences.In this paper, we introduce a new named entity recognition (NER) corpus for thecomputer programming domain, consisting of 15,372 sentences annotated with 20fine-grained entity types. We trained in-domain BERT representations(BERTOverflow) on 152 million sentences from StackOverflow, which lead to anabsolute increase of +10 F-1 score over off-the-shelf BERT. We also present theSoftNER model which achieves an overall 79.10 F$_1$ score for code and namedentity recognition on StackOverflow data. Our SoftNER model incorporates acontext-independent code token classifier with corpus-level features to improvethe BERT-based tagging model. Our code and data are available at:https://github.com/jeniyat/StackOverflowNER/

Code and Named Entity Recognition in StackOverflow