Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
Tagalog
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Annotations Creators:
expert-generated
ArXiv:
DOI:
License:
File size: 3,253 Bytes
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title: "TLUnified-NER Corpus"
description: |
- **Homepage:** [Github](https://github.com/ljvmiranda921/calamanCy)
- **Repository:** [Github](https://github.com/ljvmiranda921/calamanCy)
- **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)](https://nilsreiter.de/blog/2017/howto-annotation).
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](https://spacy.io/usage/projects) 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.
directories: ["assets", "corpus/spacy", "corpus/iob"]
vars:
version: 1.0
assets:
- dest: assets/corpus.tar.gz
description: "Annotated TLUnified corpora in spaCy format with train, dev, and test splits."
url: "https://storage.googleapis.com/ljvmiranda/calamanCy/tl_tlunified_gold/v${vars.version}/corpus.tar.gz"
workflows:
all:
- "setup-data"
- "upload-to-hf"
commands:
- name: "setup-data"
help: "Prepare the Tagalog corpora used for training various spaCy components"
script:
- mkdir -p corpus/spacy
- tar -xzvf assets/corpus.tar.gz -C corpus/spacy
- python -m spacy_to_iob corpus/spacy/ corpus/iob/
outputs:
- corpus/iob/train.iob
- corpus/iob/dev.iob
- corpus/iob/test.iob
- name: "upload-to-hf"
help: "Upload dataset to HuggingFace Hub"
script:
- git push
deps:
- corpus/iob/train.iob
- corpus/iob/dev.iob
- corpus/iob/test.iob
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