# Code and Named Entity Recognition in StackOverflow

arXiv: https://arxiv.org/abs/2005.01634 · HTML: https://mlsanity.com/paper/2005.01634/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2020.

## Built on by (most cited first, 30 of 39)

- [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023)
- [UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition](https://mlsanity.com/paper/2308.03279/) (2023)
- [GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding](https://mlsanity.com/paper/2310.14478/) (2023)
- [MosaicBERT: A Bidirectional Encoder Optimized for Fast Pretraining](https://mlsanity.com/paper/2312.17482/) (2023)
- [SpaBERT: A Pretrained Language Model from Geographic Data for Geo-Entity Representation](https://mlsanity.com/paper/2210.12213/) (2022)
- [ReadMe++: Benchmarking Multilingual Language Models for Multi-Domain Readability Assessment](https://mlsanity.com/paper/2305.14463/) (2023)
- [Few-shot Name Entity Recognition on StackOverflow](https://mlsanity.com/paper/2404.09405/) (2024)
- [De-identification of Privacy-related Entities in Job Postings](https://mlsanity.com/paper/2105.11223/) (2021)
- [A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis](https://mlsanity.com/paper/2301.08826/) (2023)
- [On the validity of pre-trained transformers for natural language processing in the software engineering domain](https://mlsanity.com/paper/2109.04738/) (2021)
- [Automatic Document Selection for Efficient Encoder Pretraining](https://mlsanity.com/paper/2210.10951/) (2022)
- [Zero-Shot Learning for Requirements Classification: An Exploratory Study](https://mlsanity.com/paper/2302.04723/) (2023)
- [Reading StackOverflow Encourages Cheating: Adding Question Text Improves Extractive Code Generation](https://mlsanity.com/paper/2106.04447/) (2021)
- [A Unified Taxonomy-Guided Instruction Tuning Framework for Entity Set Expansion and Taxonomy Expansion](https://mlsanity.com/paper/2402.13405/) (2024)
- [Can Machines Read Coding Manuals Yet? -- A Benchmark for Building Better Language Models for Code Understanding](https://mlsanity.com/paper/2109.07452/) (2021)
- [Learning to Describe Solutions for Bug Reports Based on Developer Discussions](https://mlsanity.com/paper/2110.04353/) (2021)
- [ETF: An Entity Tracing Framework for Hallucination Detection in Code Summaries](https://mlsanity.com/paper/2410.14748/) (2024)
- [CoAM: Corpus of All-Type Multiword Expressions](https://mlsanity.com/paper/2412.18151/) (2024)
- [An Empirical Study of IoT Security Aspects at Sentence-Level in Developer Textual Discussions](https://mlsanity.com/paper/2206.03079/) (2022)
- [NL2CMD: An Updated Workflow for Natural Language to Bash Commands Translation](https://mlsanity.com/paper/2302.07845/) (2023)
- [Exploring the Limits of Transfer Learning with Unified Model in the Cybersecurity Domain](https://mlsanity.com/paper/2302.10346/) (2023)
- [Leveraging Encoder-only Large Language Models for Mobile App Review Feature Extraction](https://mlsanity.com/paper/2408.01063/) (2024)
- [Entity Set Co-Expansion in StackOverflow](https://mlsanity.com/paper/2212.02271/) (2022)
- [SDLog: A Deep Learning Framework for Detecting Sensitive Information in Software Logs](https://mlsanity.com/paper/2505.14976/) (2025)
- [Bayesian Networks for Named Entity Prediction in Programming Community Question Answering](https://mlsanity.com/paper/2302.13253/) (2023)
- [Software Entity Recognition with Noise-Robust Learning](https://mlsanity.com/paper/2308.10564/) (2023)
- [Characterizing and Classifying Developer Forum Posts with their Intentions](https://mlsanity.com/paper/2312.14279/) (2023)
- [Seed-Guided Fine-Grained Entity Typing in Science and Engineering Domains](https://mlsanity.com/paper/2401.13129/) (2024)
- [Beyond Keywords: A Context-based Hybrid Approach to Mining Ethical Concern-related App Reviews](https://mlsanity.com/paper/2411.07398/) (2024)
- [How Effective are Generative Large Language Models in Performing Requirements Classification?](https://mlsanity.com/paper/2504.16768/) (2025)

## Abstract (arXiv)

There is an increasing interest in studying natural language and computer code together, as large corpora of programming texts become readily available on the Internet. For example, StackOverflow currently has over 15 million programming related questions written by 8.5 million users. Meanwhile, there is still a lack of fundamental NLP techniques for identifying code tokens or software-related named entities that appear within natural language sentences. In this paper, we introduce a new named entity recognition (NER) corpus for the computer programming domain, consisting of 15,372 sentences annotated with 20 fine-grained entity types. We trained in-domain BERT representations (BERTOverflow) on 152 million sentences from StackOverflow, which lead to an absolute increase of +10 F-1 score over off-the-shelf BERT. We also present the SoftNER model which achieves an overall 79.10 F1 score for code and named entity recognition on StackOverflow data. Our SoftNER model incorporates a context-independent code token classifier with corpus-level features to improve the BERT-based tagging model. Our code and data are available at: https://github.com/jeniyat/StackOverflowNER/