Classical architectures include convolutional neural networks (CNNs) (Zeng et al., 2014), recurrent neural networks (RNNs) (Xu et al., 2015), etc.
above methods are still insufficient for domain knowledge acquisition due to two challenges: (i) most domain entities rarely occur in the corpus, hence pattern-based methods easily suffer from the feature sparsity problem; (ii) a domain knowledge graph tends to be incomplete w.r.t.