README.md

---
license: mit
dataset_info:
  features:
  - name: id
    dtype: string
  - name: title
    dtype: string
  - name: abstract
    dtype: string
  - name: classification_labels
    sequence: string
  - name: numerical_classification_labels
    sequence: int64
  splits:
  - name: train
    num_bytes: 235500446
    num_examples: 178521
  - name: test
    num_bytes: 1175810
    num_examples: 828
  download_size: 116387254
  dataset_size: 236676256
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
task_categories:
- text-classification
language:
- en
pretty_name: NLP Taxonomy Data
size_categories:
- 100K<n<1M
tags:
- science
- scholarly
---


# NLP Taxonomy Classification Data

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.

📄 Paper: [Exploring the Landscape of Natural Language Processing Research (RANLP 2023)](https://aclanthology.org/2023.ranlp-1.111)

💻 GitHub: [https://github.com/sebischair/Exploring-NLP-Research](https://github.com/sebischair/Exploring-NLP-Research)

🤗 Model: [https://huggingface.co/TimSchopf/nlp_taxonomy_classifier](https://huggingface.co/TimSchopf/nlp_taxonomy_classifier)

<a name="#nlp-taxonomy"/></a>
## NLP Taxonomy

![NLP taxonomy](https://github.com/sebischair/Exploring-NLP-Research/blob/main/figures/NLP-Taxonomy.jpg?raw=true)

A machine readable version of the NLP taxonomy is available in our code repository as an OWL file: [https://github.com/sebischair/Exploring-NLP-Research/blob/main/NLP-Taxonomy.owl](https://github.com/sebischair/Exploring-NLP-Research/blob/main/NLP-Taxonomy.owl)

For our work on [NLP-KG](https://aclanthology.org/2024.acl-demos.13), we extended this taxonomy to a large hierarchy of fields of study in NLP and made it available in a machine readable format as an OWL file at: [https://github.com/NLP-Knowledge-Graph/NLP-KG-WebApp](https://github.com/NLP-Knowledge-Graph/NLP-KG-WebApp)

## Citation information
When citing our work in academic papers and theses, please use this BibTeX entry:
``` 
@inproceedings{schopf-etal-2023-exploring,
    title = "Exploring the Landscape of Natural Language Processing Research",
    author = "Schopf, Tim  and
      Arabi, Karim  and
      Matthes, Florian",
    editor = "Mitkov, Ruslan  and
      Angelova, Galia",
    booktitle = "Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing",
    month = sep,
    year = "2023",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://aclanthology.org/2023.ranlp-1.111",
    pages = "1034--1045",
    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.",
}
```