Multi-Task Learning with Contextualized Word Representations for Extented Named Entity Recognition
This addresses the need for automated FG-NER in NLP applications, offering a solution that reduces manual effort compared to previous methods.
The paper tackles the problem of Fine-Grained Named Entity Recognition (FG-NER) by investigating multi-task learning with contextualized word representations, achieving state-of-the-art results without manual data creation or feature design.
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.