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--- |
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license: mit |
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dataset_info: |
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features: |
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- name: id |
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dtype: string |
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- name: title |
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dtype: string |
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- name: abstract |
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dtype: string |
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- name: classification_labels |
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sequence: string |
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- name: numerical_classification_labels |
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sequence: int64 |
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splits: |
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- name: train |
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num_bytes: 235500446 |
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num_examples: 178521 |
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- name: test |
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num_bytes: 1175810 |
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num_examples: 828 |
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download_size: 116387254 |
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dataset_size: 236676256 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: test |
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path: data/test-* |
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task_categories: |
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- text-classification |
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language: |
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- en |
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pretty_name: NLP Taxonomy Data |
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size_categories: |
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- 100K<n<1M |
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--- |
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# NLP Taxonomy Classification Data |
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The dataset consists of titles and abstracts from NLP-related papers. Each paper is annotated with multiple fields of study from the [NLP taxonomy](#nlp-taxonomy). Each sample is annotated with all possible lower-level concepts and their hypernyms in the [NLP taxonomy](#nlp-taxonomy). The training dataset contains 178,521 weakly annotated samples. The test dataset consists of 828 manually annotated samples from the EMNLP22 conference. The manually labeled test dataset might not contain all possible classes since it consists of EMNLP22 papers only, and some rarer classes haven’t been published there. Therefore, we advise creating an additional test or validation set from the train data that includes all the possible classes. |
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📄 Paper: [Exploring the Landscape of Natural Language Processing Research (RANLP 2023)](https://aclanthology.org/2023.ranlp-1.111) |
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💻 Code: [https://github.com/sebischair/Exploring-NLP-Research](https://github.com/sebischair/Exploring-NLP-Research) |
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🤗 Model: [https://huggingface.co/TimSchopf/nlp_taxonomy_classifier](https://huggingface.co/TimSchopf/nlp_taxonomy_classifier) |
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<a name="#nlp-taxonomy"/></a> |
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## NLP Taxonomy |
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## Citation information |
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When citing our work in academic papers and theses, please use this BibTeX entry: |
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``` |
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@inproceedings{schopf-etal-2023-exploring, |
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title = "Exploring the Landscape of Natural Language Processing Research", |
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author = "Schopf, Tim and |
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Arabi, Karim and |
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Matthes, Florian", |
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editor = "Mitkov, Ruslan and |
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Angelova, Galia", |
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booktitle = "Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing", |
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month = sep, |
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year = "2023", |
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address = "Varna, Bulgaria", |
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publisher = "INCOMA Ltd., Shoumen, Bulgaria", |
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url = "https://aclanthology.org/2023.ranlp-1.111", |
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pages = "1034--1045", |
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abstract = "As an efficient approach to understand, generate, and process natural language texts, research in natural language processing (NLP) has exhibited a rapid spread and wide adoption in recent years. Given the increasing research work in this area, several NLP-related approaches have been surveyed in the research community. However, a comprehensive study that categorizes established topics, identifies trends, and outlines areas for future research remains absent. Contributing to closing this gap, we have systematically classified and analyzed research papers in the ACL Anthology. As a result, we present a structured overview of the research landscape, provide a taxonomy of fields of study in NLP, analyze recent developments in NLP, summarize our findings, and highlight directions for future work.", |
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} |
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``` |
