NER with Bidirectional LSTM-CNNs
Year: 2,016
Journal: Transactions of the Association for Computational Linguistics
Languages: English
Programming languages: Python
Input data:
sentences/words
Output data:
named entity tag scores
This paper presents a novel neural network architecture that automatically detects word- and character-level features using a hybrid bidirectional LSTM and CNN architecture, eliminating the need for most feature engineering.