DOI: 10.25835/hodc41f5
title: CS-NER

description: # Computer Science Named Entity Recognition in the Open Research Knowledge Graph ### 1) About This work proposes a standardized CS-NER task by defining a set of seven _contribution-centric_ scholarly entities for CS NER viz., _research problem_ , _solution_ , _resource_ , _language_ , _tool_ , _method_ , and _dataset_ . The main contributions are: 1) Merges annotations for contribution-centric named entities from related work as the following datasets: - The dataset proposed in [Analyzing the Dynamics of Research by Extracting Key Aspects of Scientific Papers](https://aclanthology.org/I11-1001/) (Gupta & Manning, IJCNLP 2011) is the source for [ftd](https://github.com/jd-coderepos/contributions-ner-cs/tree/main/ftd), annotated for both titles and abstracts for the following select entities mapped to our standardized types _focus_ -&gt; _solution_ ; _domain_ -&gt; _research problem_ ; and _technique_ -&gt; _method_ - The dataset proposed in [Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction](https://aclanthology.org/D18-1360/) (Luan et al., EMNLP 2018) is the source for [scierc](https://github.com/jd-coderepos/contributions-ner-cs/tree/main/scierc), annotated for abstracts for the following select entities with mappings _task_ -&gt; _research problem_ - The dataset proposed in [SemEval-2021 Task 11: NLPContributionGraph - Structuring Scholarly NLP Contributions for a Research Knowledge Graph](https://aclanthology.org/2021.semeval-1.44/) (D’Souza et al., SemEval 2021) is the source for [ncg](https://github.com/jd-coderepos/contributions-ner-cs/tree/main/ncg), annotated for both titles and abstracts for _research problem_ - https://paperswithcode.com/ as the [pwc](https://github.com/jd-coderepos/contributions-ner-cs/tree/main/pwc) annotated for both titles and abstracts for _task_ -&gt; _research problem_ and _method_ entities. 2) Additionally, supplies a new annotated dataset for the titles in the ACL anthology in the [acl repository](https://github.com/jd-coderepos/contributions-ner-cs/tree/main/acl) where titles are annotated with all seven entities. ### 2) Dataset Statistics for [full dataset](https://github.com/jd-coderepos/contributions-ner-cs/tree/main/full%20dataset) #### Titles `train.data` | NER | Count | | --- | --- | | solution | 65,213 | | research problem | 43,033 | | resource | 19,759 | | method | 19,645 | | tool | 4,856 | | dataset | 4,062 | | language | 1,704 | `dev.data` | NER | Count | | --- | --- | | solution | 3,685 | | research problem | 2,717 | | resource | 1,224 | | method | 1,172 | | tool | 264 | | dataset | 191 | | language | 79 | `test.data` | NER | Count | | --- | --- | | solution | 29,287 | | research problem | 11,093 | | resource | 8,511 | | method | 7,009 | | tool | 2,272 | | dataset | 947 | | language | 690 | #### Abstracts `train-abs.data` | NER | Count | | --- | --- | | research problem | 15,498 | | method | 12,932 | `dev-abs.data` | NER | Count | | --- | --- | | research problem | 1,450 | | method | 839 | `test-abs.data` | NER | Count | | --- | --- | | research problem | 4,123 | | method | 3,170 | The reamining repositories have specialized README files with the respective dataset statistics. ### 3) Citation Accepted for publication in [ICADL 2022](https://icadl.net/icadl2022/) proceedings. `Citation information forthcoming` Preprint ``` @article{d2022computer, title={Computer Science Named Entity Recognition in the Open Research Knowledge Graph}, author={D'Souza, Jennifer and Auer, S{\"o}ren}, journal={arXiv preprint arXiv:2203.14579}, year={2022} } ``` ### 4) Additional resources #### CS NER Software trained on the dataset in this repository Codebase: https://gitlab.com/TIBHannover/orkg/nlp/orkg-nlp-experiments/-/tree/master/orkg_cs_ner Service URL - REST API: https://orkg.org/nlp/api/docs#/annotation/annotates_paper_annotation_csner_post Service URL - PyPi: https://orkg-nlp-pypi.readthedocs.io/en/latest/services/services.html#cs-ner-computer-science-named-entity-recognition