Hierarchical Contextualized Representation for Named Entity Recognition

Understand

Named entity recognition (NER) models are typically based on the architecture of Bi-directional LSTM (BiLSTM).

• The constraints of sequential nature and the modeling of single input prevent the full utilization of global information from larger scope, not only in the entire sentence, but also in the entire document (dataset).
• In this paper, we address these two deficiencies and propose a model augmented with hierarchical contextualized representation: sentence-level representation and document-level representation.
• In sentence-level, we take different contributions of words in a single sentence into consideration to enhance the sentence representation learned from an independent BiLSTM via label embedding attention mechanism.