Angrosh Mandya, Danushka Bollegala, Frans Coenen, Katie Atkinson.

doi:10.24432/C5RP44

TL;DR

We propose in this paper a combined model of Long Short Term Memory and Convolutional Neural Networks (LSTM_CNN) model that exploits word embeddings and positional embeddings for cross-sentence n-ary relation extraction. The proposed model brings together the properties of both LSTMs and CNNs, to simultaneously exploit long-range sequential information and capture most informative features, essential for cross-sentence n-ary relation extraction. The LSTM_CNN model is evaluated on standard datasets on cross-sentence n-ary relation extraction, where it significantly outperforms baselines such as CNNs, LSTMs and also a combined CNN_LSTM model. The paper also shows that the proposed LSTM_CNN model outperforms the current state-of-the-art methods on cross-sentence n-ary relation extraction.

Citation

@inproceedings{
mandya2019combining,
title={Combining Long Short Term Memory and Convolutional Neural Network for Cross-Sentence n-ary Relation Extraction},
author={Angrosh Mandya and Danushka Bollegala and Frans Coenen and Katie Atkinson},
booktitle={Automated Knowledge Base Construction (AKBC)},
year={2019},
url={https://openreview.net/forum?id=Sye0lZqp6Q},
doi={10.24432/C5RP44}
}