Named Entity Recognition with Bidirectional LSTM-CNNs

Named entity recognition is a challenging task that has traditionally required large amounts of knowledge in the form of feature engineering and lexicons to achieve high performance. In this paper, we present 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... (read more)

PDF Abstract TACL 2016 PDF TACL 2016 Abstract
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Named Entity Recognition CoNLL 2003 (English) Bi-LSTM-CNN F1 91.62 # 38
Named Entity Recognition Ontonotes v5 (English) Chiu and Nichols (2016) F1 86.19 # 19

Methods used in the Paper


METHOD TYPE
Sigmoid Activation
Activation Functions
Tanh Activation
Activation Functions
BiLSTM
Bidirectional Recurrent Neural Networks
Convolution
Convolutions
CNN BiLSTM
Bidirectional Recurrent Neural Networks
LSTM
Recurrent Neural Networks