Improving Neural Sequence Labelling using Additional Linguistic Information
This work addresses sequence labeling tasks in NLP, such as POS tagging and NER, by enhancing neural models with linguistic information, representing an incremental improvement over existing methods.
The authors tackled the problem of neural sequence labeling by incorporating additional linguistic features like sense embeddings and selective character embeddings to improve performance, achieving state-of-the-art results on benchmark datasets for POS tagging, NER, and chunking with a significantly better convergence rate.
Sequence labelling is the task of assigning categorical labels to a data sequence. In Natural Language Processing, sequence labelling can be applied to various fundamental problems, such as Part of Speech (POS) tagging, Named Entity Recognition (NER), and Chunking. In this study, we propose a method to add various linguistic features to the neural sequence framework to improve sequence labelling. Besides word level knowledge, sense embeddings are added to provide semantic information. Additionally, selective readings of character embeddings are added to capture contextual as well as morphological features for each word in a sentence. Compared to previous methods, these added linguistic features allow us to design a more concise model and perform more efficient training. Our proposed architecture achieves state of the art results on the benchmark datasets of POS, NER, and chunking. Moreover, the convergence rate of our model is significantly better than the previous state of the art models.