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

Nov 17, 2018 AKBC 2019 Conference Blind Submission readers: everyone Show Bibtex
  • Keywords: n-ary relation extraction, information extraction
  • Abstract: We propose in this paper a combined model of Long Short Term Memory and Convolutional Neural Networks (LSTM_CNN) model 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 datasets 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 proposed LSTM_CNN model outperforms the current state-of-the-art methods on cross-sentence n-ary relation extraction.
  • Archival Status: Archival
  • Subject Areas: Information Extraction, Applications: Biomedicine
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