README.md

metadata

annotations_creators:
  - expert-generated
language:
  - tl
license: gpl-3.0
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10K
task_categories:
  - token-classification
task_ids:
  - named-entity-recognition
pretty_name: TLUnified-NER
tags:
  - low-resource
  - named-entity-recognition
dataset_info:
  features:
    - name: id
      dtype: string
    - name: tokens
      sequence: string
    - name: ner_tags
      sequence:
        class_label:
          names:
            '0': O
            '1': B-PER
            '2': I-PER
            '3': B-ORG
            '4': I-ORG
            '5': B-LOC
            '6': I-LOC
  splits:
    - name: train
      num_bytes: 3380392
      num_examples: 6252
    - name: validation
      num_bytes: 427069
      num_examples: 782
    - name: test
      num_bytes: 426247
      num_examples: 782
  download_size: 971039
  dataset_size: 4233708
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*
train-eval-index:
  - config: conllpp
    task: token-classification
    task_id: entity_extraction
    splits:
      train_split: train
      eval_split: test
    col_mapping:
      tokens: tokens
      ner_tags: tags
    metrics:
      - type: seqeval
        name: seqeval

🪐 spaCy Project: TLUnified-NER Corpus

• Homepage: Github
• Repository: Github
• Point of Contact: ljvmiranda@gmail.com

Dataset Summary

This dataset contains the annotated TLUnified corpora from Cruz and Cheng (2021). It is a curated sample of around 7,000 documents for the named entity recognition (NER) task. The majority of the corpus are news reports in Tagalog, resembling the domain of the original ConLL 2003. There are three entity types: Person (PER), Organization (ORG), and Location (LOC).

Dataset	Examples	PER	ORG	LOC
Train	6252	6418	3121	3296
Development	782	793	392	409
Test	782	818	423	438

Data Fields

The data fields are the same among all splits:

• id: a string feature
• tokens: a list of string features.
• ner_tags: a list of classification labels, with possible values including O (0), B-PER (1), I-PER (2), B-ORG (3), I-ORG (4), B-LOC (5), I-LOC (6)

Annotation process

The author, together with two more annotators, labeled curated portions of TLUnified in the course of four months. All annotators are native speakers of Tagalog. For each annotation round, the annotators resolved disagreements, updated the annotation guidelines, and corrected past annotations. They followed the process prescribed by Reiters (2017).

They also measured the inter-annotator agreement (IAA) by computing pairwise comparisons and averaging the results:

• Cohen's Kappa (all tokens): 0.81
• Cohen's Kappa (annotated tokens only): 0.65
• F1-score: 0.91

About this repository

This repository is a spaCy project for converting the annotated spaCy files into IOB. The process goes like this: we download the raw corpus from Google Cloud Storage (GCS), convert the spaCy files into a readable IOB format, and parse that using our loading script (i.e., tlunified-ner.py). We're also shipping the IOB file so that it's easier to access.

📋 project.yml

The project.yml defines the data assets required by the project, as well as the available commands and workflows. For details, see the spaCy projects documentation.

⏯ Commands

The following commands are defined by the project. They can be executed using spacy project run [name]. Commands are only re-run if their inputs have changed.

Command	Description
setup-data	Prepare the Tagalog corpora used for training various spaCy components
upload-to-hf	Upload dataset to HuggingFace Hub

⏭ Workflows

The following workflows are defined by the project. They can be executed using spacy project run [name] and will run the specified commands in order. Commands are only re-run if their inputs have changed.

Workflow	Steps
all	setup-data → upload-to-hf

🗂 Assets

The following assets are defined by the project. They can be fetched by running spacy project assets in the project directory.

File	Source	Description
assets/corpus.tar.gz	URL	Annotated TLUnified corpora in spaCy format with train, dev, and test splits.

Citation

You can cite this dataset as:

@misc{miranda2023developing,
  title={Developing a Named Entity Recognition Dataset for Tagalog}, 
  author={Lester James V. Miranda},
  year={2023},
  eprint={2311.07161},
  archivePrefix={arXiv},
  primaryClass={cs.CL}
}