66 citations · 107 across the 6 of their papers we have counts for
5 papers · 1 filter
End-to-end neural relation extraction using deep biaffine attention
Dat Quoc Nguyen, Karin Verspoor
We propose a neural network model for joint extraction of named entities and relations between them, without any hand-crafted features. The key contribution of our model is to exte…
Comparing CNN and LSTM character-level embeddings in BiLSTM-CRF models for chemical and disease named entity recognition
Zenan Zhai, Dat Quoc Nguyen, Karin Verspoor
We compare the use of LSTM-based and CNN-based character-level word embeddings in BiLSTM-CRF models to approach chemical and disease named entity recognition (NER) tasks. Empirical…
An improved neural network model for joint POS tagging and dependency parsing
Dat Quoc Nguyen, Karin Verspoor
We propose a novel neural network model for joint part-of-speech (POS) tagging and dependency parsing. Our model extends the well-known BIST graph-based dependency parser (Kiperwas…
From POS tagging to dependency parsing for biomedical event extraction
Dat Quoc Nguyen, Karin Verspoor
Background: Given the importance of relation or event extraction from biomedical research publications to support knowledge capture and synthesis, and the strong dependency of appr…
Convolutional neural networks for chemical-disease relation extraction are improved with character-based word embeddings
Dat Quoc Nguyen, Karin Verspoor
We investigate the incorporation of character-based word representations into a standard CNN-based relation extraction model. We experiment with two common neural architectures, CN…