Combining Long Short Term Memory and Convolutional Neural Network for Cross-Sentence n-ary Relation Extraction
This addresses relation extraction across multiple sentences for natural language processing applications, but appears incremental as it combines existing neural network components.
The paper tackled cross-sentence n-ary relation extraction by proposing an LSTM-CNN model that combines word and positional embeddings, resulting in significant performance improvements over baselines and state-of-the-art methods.
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.