Distant Supervision and Noisy Label Learning for Low Resource Named Entity Recognition: A Study on Hausa and Yorùbá

Abstract

The lack of labeled training data has limited the development of natural language processing tools, such as named entity recognition, for many languages spoken in developing countries. Techniques such as distant and weak supervision can be used to create labeled data in a (semi-) automatic way. Additionally, to alleviate some of the negative effects of the errors in automatic annotation, noise-handling methods can be integrated. Pretrained word embeddings are another key component of most neural named entity classifiers. With the advent of more complex contextual word embeddings, an interesting trade-off between model size and performance arises. While these techniques have been shown to work well in high-resource settings, we want to study how they perform in low-resource scenarios. In this work, we perform named entity recognition for Hausa and Yorùbá, two languages that are widely spoken in several developing countries. We evaluate different embedding approaches and show that distant supervision can be successfully leveraged in a realistic low-resource scenario where it can more than double a classifier's performance.

Accepted to ICLR 2020 Workshop

References in corpus (6)

• Efficient Estimation of Word Representations in Vector Space
• Snorkel: Rapid Training Data Creation with Weak Supervision
• Enriching Word Vectors with Subword Information
• Image Classification with Deep Learning in the Presence of Noisy Labels: A Survey
• WebVision Database: Visual Learning and Understanding from Web Data
• Massive vs. Curated Word Embeddings for Low-Resourced Languages. The Case of Yorùbá and Twi

Cited by in corpus (2)

• ANEA: Distant Supervision for Low-Resource Named Entity Recognition
• Few-shot Named Entity Recognition with Cloze Questions