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In order to improve the accuracy and recall rate of term extraction results in the Chinese patent domain, approaching from the perspective of deep learning, with part-of-speech and dependency relationships as features, a patent domain term extration model (Bi-LSTM-CRF) was proposed by combining Conditional Random Fields (CRF) and bi-directional long short-term memory (Bi-LSTM) based on a multi-feature fusion. Based on the two explicit characteristics of part of speech and dependency, the double-layer bidirectional LSTM neural network was used to mine the temporal and semantic information in the data, which overcame the disadvantages of the traditional methods, such as weak generality and inability to capture the implicit information in the context as well as addressing the dependency relationship among the output tags through the CRF layer. Experimental results show that this deep learning method is effective in terms of domain term extraction.
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