Combining Long Short Term Memory and Convolutional Neural Network for Cross-Sentence n-ary Relation Extraction
Abstract
We propose in this paper a combined model of Long Short Term Memory and Convolutional Neural Networks (LSTM-CNN) 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 dataset 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 LSTM-CNN model outperforms the current state-of-the-art methods on cross-sentence n-ary relation extraction.
Keywords
Cite
@article{arxiv.1811.00845,
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},
journal= {arXiv preprint arXiv:1811.00845},
year = {2018}
}