  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.
