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