pytorch-partial-tagger 0.1.8

Sequence Tagger for Partially Annotated Dataset in PyTorch

pytorch-partial-tagger

This is a library to build a CRF tagger for a partially annotated dataset in PyTorch. You can build your own NER tagger only from dictionary. The algorithm of this tagger is based on Effland and Collins. (2021).

Usage

Import all dependencies first:

import torch

from partial_tagger.data import CharBasedTags
from partial_tagger.metric import Metric
from partial_tagger.utils import create_tag, create_trainer

Prepare your own datasets. Each item of dataset must have a string and tags. A string represents text below. Tags represent a collection of tags, where each tag has a start, a length, and a label, which are defined as tags below. A start represents a position in text where a tag starts. A length represents a distance in text between the beginning of a tag and the end of a tag. A label represents what you want to assign to a span of text defined by a start and a length.

from partial_tagger.utils import create_tags, CharBasedTags


text = "Tokyo is the capital of Japan."
tags = CharBasedTags(
    (
        create_tag(start=0, length=5, label="LOC"),  # Tag for Tokyo
        create_tag(start=24, length=5, label="LOC")  # Tag for Japan
    ),
    text
)
train_dataset = [(text, tags), ...]
validation_dataset = [...]
test_dataset = [...]

Here, you would train your tagger and evaluate its performance.

You could train your own tagger by initializing Trainer and passing datasets to it. After training, trainer gives you Recognizer object which predicts character-based tags from given texts.

You could evaluate the performance of your tagger using Metric as below.

device = torch.device("cuda")

trainer = create_trainer()
recognizer = trainer(train_dataset, validation_dataset, device)

texts, ground_truths = zip(*test_dataset)

batch_size = 15
predictions = recognizer(texts, batch_size, device)

metric = Metric()
metric(predictions, ground_truths)

print(metric.get_scores())  # Display F1-score, Precision, Recall

Installation

pip install pytorch-partial-tagger

References

• Yuta Tsuboi, Hisashi Kashima, Shinsuke Mori, Hiroki Oda, and Yuji Matsumoto. 2008. Training Conditional Random Fields Using Incomplete Annotations. In Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), pages 897–904, Manchester, UK. Coling 2008 Organizing Committee.
• Alexander Rush. 2020. Torch-Struct: Deep Structured Prediction Library. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 335–342, Online. Association for Computational Linguistics.
• Thomas Effland and Michael Collins. 2021. Partially Supervised Named Entity Recognition via the Expected Entity Ratio Loss. Transactions of the Association for Computational Linguistics, 9:1320–1335.