Fine-Grained Named Entity Recognition (FG-NER) is critical for many NLP applications. While classical named entity recognition (NER) has attracted a substantial amount of research, FG-NER is still an open research domain. The current state-of-the-art (SOTA) model for FG-NER relies heavily on manual efforts for building a dictionary and designing hand-crafted features. The end-to-end framework which achieved the SOTA result for NER did not get the competitive result compared to SOTA model for FG-NER. In this paper, we investigate how effective multi-task learning approaches are in an end-to-end framework for FG-NER in different aspects. Our experiments show that using multi-task learning approaches with contextualized word representation can help an end-to-end neural network model achieve SOTA results without using any additional manual effort for creating data and designing features.
@article{arxiv.1902.10118,
title = {Multi-Task Learning with Contextualized Word Representations for Extented Named Entity Recognition},
author = {Thai-Hoang Pham and Khai Mai and Nguyen Minh Trung and Nguyen Tuan Duc and Danushka Bolegala and Ryohei Sasano and Satoshi Sekine},
journal= {arXiv preprint arXiv:1902.10118},
year = {2019}
}