Empower Sequence Labeling with Task-Aware Neural Language Model

Understand

Linguistic sequence labeling is a general modeling approach that encompasses a variety of problems, such as part-of-speech tagging and named entity recognition.

• Recent advances in neural networks (NNs) make it possible to build reliable models without handcrafted features.
• However, in many cases, it is hard to obtain sufficient annotations to train these models.
• In this study, we develop a novel neural framework to extract abundant knowledge hidden in raw texts to empower the sequence labeling task.