Mandya, Angrosh, Bollegala, Danushka, Coenen, Frans and Atkinson, Katie
(2018)
Combining Long Short Term Memory and Convolutional Neural Network for
Cross-Sentence n-ary Relation Extraction.
CoRR, abs/18.
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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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | cs.IR, cs.IR, cs.CL |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 27 Feb 2020 10:26 |
| Last Modified: | 19 Jan 2023 00:43 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3042462 |
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