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arXiv · 1811.00845

Combining Long Short Term Memory and Convolutional Neural Network for Cross-Sentence n-ary Relation Extraction

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

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Angrosh Mandya, Danushka Bollegala, Frans Coenen, Katie Atkinson. 2018-11-02. Combining Long Short Term Memory and Convolutional Neural Network for Cross-Sentence n-ary Relation Extraction. https://arxiv.org/abs/1811.00845

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