Datasets:
Tasks:
Token Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
named-entity-recognition
Languages:
Tagalog
Size:
1K - 10K
ArXiv:
DOI:
License:
| title: "TLUnified-NER Corpus" | |
| description: | | |
| - **Homepage:** [Github](https://github.com/ljvmiranda921/calamanCy) | |
| - **Repository:** [Github](https://github.com/ljvmiranda921/calamanCy) | |
| - **Point of Contact:** [email protected] | |
| ### 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 | |