---
license: mit
language: 
- en 
task_categories:
- text-classification
- token-classification
- other
task_ids:
- named-entity-recognition
- text-scoring
pretty_name: Dubliners (James Joyce)
description: |
  A dataset of James Joyce's collection of short stories "Dubliners," prepared for NLP tasks and computational analysis of literary texts. The dataset includes:
  - Text tokenized by sentences.
  - POS-tagged sentences using NLTK.
  - Results of analyzing the text with spaCy (POS-tagged, named entities, dependencies).

  This dataset was created as part of an NLP course at the Higher School of Economics (HSE). For more details, see the original repository: https://github.com/vifirsanova/compling.
  
  The dataset can be used for various NLP tasks, including:
  - Part-of-speech tagging.
  - Named entity recognition.
  - Dependency parsing.
  - Computational analysis of literary texts.
  
  It is particularly suited for researchers and students interested in computational linguistics and literary analysis.

size_categories:
- 10K<n<100K
source_datasets:
- original
dataset_info:
  features:
  - name: text
    dtype: string
    description: Raw text from "Dubliners," tokenized by sentences.
  - name: nltk_pos
    dtype: string
    description: Part-of-speech tags for each sentence, generated using NLTK.
  - name: spacy_pos
    dtype: string
    description: Part-of-speech tags for each sentence, generated using spaCy.
  - name: named_entities
    dtype: string
    description: Named entities identified in the text, generated using spaCy.
  - name: dependencies
    dtype: string
    description: Dependency parses for each sentence, generated using spaCy.
  splits:
  - name: train
    num_bytes: 1024000
    num_examples: 1000
  download_size: 512000
  dataset_size: 1024000
tags:
- literature
- nlp
- pos-tagging
- named-entity-recognition
- dependency-parsing
- james-joyce
- dubliners
- computational-linguistics
---

# Dataset Card for Dubliners (James Joyce)

## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Structure](#dataset-structure)
- [Usage](#usage)
- [License](#license)
- [Citation](#citation)

## Dataset Description
- **Homepage:** [GitHub Repository](https://github.com/docsportellochrys/nlp-learning)
- **Repository:** [GitHub](https://github.com/docsportellochrys/nlp-learning/tree/main/3.text_preprocessing/)
- **Point of Contact:** [20chryskylodon09@gmail.com]
- **License:** MIT

### Dataset Summary
This dataset contains James Joyce's collection of short stories "Dubliners," prepared for NLP tasks and computational analysis. It includes:
- Text tokenized by sentences.
- POS-tagged sentences using NLTK.
- Results of analyzing the text with spaCy (POS-tagged, named entities, dependencies).

### Supported Tasks
- Part-of-speech tagging
- Named entity recognition
- Dependency parsing
- Computational analysis of literary texts

## Dataset Structure
### Data Fields
- `text`: Raw text from "Dubliners," tokenized by sentences.
- `nltk_pos`: Part-of-speech tags for each sentence, generated using NLTK.
- `spacy_pos`: Part-of-speech tags for each sentence, generated using spaCy.
- `named_entities`: Named entities identified in the text, generated using spaCy.
- `dependencies`: Dependency parses for each sentence, generated using spaCy.

### Data Splits
- `train`: Contains the entire dataset.

## Usage
This dataset is intended for use in NLP tasks such as part-of-speech tagging, named entity recognition, dependency parsing, and computational analysis of literary texts. It is particularly suited for researchers and students interested in computational linguistics and literary analysis.

## License
This dataset is licensed under the MIT License.

## Citation
If you use this dataset, please cite the original source:
```bibtex
@misc{dubliners-nlp-dataset,
  author = {doc_sportello},
  title = {Dubliners (James Joyce) NLP Dataset},
  year = {2025},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/docsportellochrys/nlp-learning}},
}