# Computer Science Named Entity Recognition in the Open Research Knowledge Graph arXiv: https://arxiv.org/abs/2203.14579 ยท HTML: https://mlsanity.com/paper/2203.14579/ ## Key facts - Cited by 9 papers in the map (data through 14 Oct 2026). - Builds on 25 of its 94 references that are in the map. - First posted on arXiv 28 Mar 2022. ## Builds on (most cited first, 25 of 25) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019) - [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016) - [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016) - [Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction](https://mlsanity.com/paper/1808.09602/) (2018) - [Named Entity Recognition with Bidirectional LSTM-CNNs](https://mlsanity.com/paper/1511.08308/) (2015) - [Character-Aware Neural Language Models](https://mlsanity.com/paper/1508.06615/) (2015) - [SciREX: A Challenge Dataset for Document-Level Information Extraction](https://mlsanity.com/paper/2005.00512/) (2020) - [SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications](https://mlsanity.com/paper/1704.02853/) (2017) - [Semi-supervised sequence tagging with bidirectional language models](https://mlsanity.com/paper/1705.00108/) (2017) - [MedMentions: A Large Biomedical Corpus Annotated with UMLS Concepts](https://mlsanity.com/paper/1902.09476/) (2019) - [Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references](https://mlsanity.com/paper/1402.4578/) (2014) - [Identification of Tasks, Datasets, Evaluation Metrics, and Numeric Scores for Scientific Leaderboards Construction](https://mlsanity.com/paper/1906.09317/) (2019) - [The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structures](https://mlsanity.com/paper/1905.06939/) (2019) - [Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks](https://mlsanity.com/paper/1703.06345/) (2017) - [NCRF++: An Open-source Neural Sequence Labeling Toolkit](https://mlsanity.com/paper/1806.05626/) (2018) - [NeuroNER: an easy-to-use program for named-entity recognition based on neural networks](https://mlsanity.com/paper/1705.05487/) (2017) - [End-to-End NLP Knowledge Graph Construction](https://mlsanity.com/paper/2106.01167/) (2021) - [An Annotated Corpus for Machine Reading of Instructions in Wet Lab Protocols](https://mlsanity.com/paper/1805.00195/) (2018) - [SemEval-2021 Task 11: NLPContributionGraph -- Structuring Scholarly NLP Contributions for a Research Knowledge Graph](https://mlsanity.com/paper/2106.07385/) (2021) - [Automated Mining of Leaderboards for Empirical AI Research](https://mlsanity.com/paper/2109.13089/) (2021) - [Annotating and Extracting Synthesis Process of All-Solid-State Batteries from Scientific Literature](https://mlsanity.com/paper/2002.07339/) (2020) - [The STEM-ECR Dataset: Grounding Scientific Entity References in STEM Scholarly Content to Authoritative Encyclopedic and Lexicographic Sources](https://mlsanity.com/paper/2003.01006/) (2020) - [An Investigation of Recurrent Neural Architectures for Drug Name Recognition](https://mlsanity.com/paper/1609.07585/) (2016) - [Pattern-based Acquisition of Scientific Entities from Scholarly Article Titles](https://mlsanity.com/paper/2109.00199/) (2021) ## Built on by (most cited first, 9 of 9) - [GSAP-NER: A Novel Task, Corpus, and Baseline for Scholarly Entity Extraction Focused on Machine Learning Models and Datasets](https://mlsanity.com/paper/2311.09860/) (2023) - [ORKG-Leaderboards: A Systematic Workflow for Mining Leaderboards as a Knowledge Graph](https://mlsanity.com/paper/2305.11068/) (2023) - [Mining for Species, Locations, Habitats, and Ecosystems from Scientific Papers in Invasion Biology: A Large-Scale Exploratory Study with Large Language Models](https://mlsanity.com/paper/2501.18287/) (2025) - [Clustering Semantic Predicates in the Open Research Knowledge Graph](https://mlsanity.com/paper/2210.02034/) (2022) - [Astro-NER -- Astronomy Named Entity Recognition: Is GPT a Good Domain Expert Annotator?](https://mlsanity.com/paper/2405.02602/) (2024) - [Overview of STEM Science as Process, Method, Material, and Data Named Entities](https://mlsanity.com/paper/2205.11863/) (2022) - [Instruction Finetuning for Leaderboard Generation from Empirical AI Research](https://mlsanity.com/paper/2408.10141/) (2024) - [Charting the Future of Scholarly Knowledge with AI: A Community Perspective](https://mlsanity.com/paper/2509.02581/) (2025) - [Nested Named Entity Recognition in Plasma Physics Research Articles](https://mlsanity.com/paper/2602.11163/) (2026) ## Abstract (arXiv) Domain-specific named entity recognition (NER) on Computer Science (CS) scholarly articles is an information extraction task that is arguably more challenging for the various annotation aims that can beset the task and has been less studied than NER in the general domain. Given that significant progress has been made on NER, we believe that scholarly domain-specific NER will receive increasing attention in the years to come. Currently, progress on CS NER -- the focus of this work -- is hampered in part by its recency and the lack of a standardized annotation aim for scientific entities/terms. This work proposes a standardized task by defining a set of seven contribution-centric scholarly entities for CS NER viz., research problem, solution, resource, language, tool, method, and dataset. Following which, its main contributions are: combines existing CS NER resources that maintain their annotation focus on the set or subset of contribution-centric scholarly entities we consider; further, noting the need for big data to train neural NER models, this work additionally supplies thousands of contribution-centric entity annotations from article titles and abstracts, thus releasing a cumulative large novel resource for CS NER; and, finally, trains a sequence labeling CS NER model inspired after state-of-the-art neural architectures from the general domain NER task. Throughout the work, several practical considerations are made which can be useful to information technology designers of the digital libraries.