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tlunified-ner

Tasks:
Token Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
named-entity-recognition
Languages:
Tagalog
Size:
1K - 10K
ArXiv:
Tags:
low-resource
named-entity-recognition
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metadata
license: gpl-3.0
task_categories:
  - token-classification
task_ids:
  - named-entity-recognition
language:
  - tl
size_categories:
  - 1K<n<10K
pretty_name: TLUnified-NER
tags:
  - low-resource
  - named-entity-recognition
annotations_creators:
  - expert-generated
multilinguality:
  - monolingual
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

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.