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21 December 2021 Incorporating lexicon knowledge into Chinese NER using hierarchical meta-embedding
Shuo Liu, Yinliang Zhao
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Proceedings Volume 12156, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021); 121560J (2021) https://doi.org/10.1117/12.2626480
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021), 2021, Sanya, China
Abstract
Integrating lexicon knowledge into character-based methods can improve the performance of neural network models for Chinese named entity recognition (NER). For example, Lattice LSTM [1]and WC-LSTM [2] perform well on several public Chinese NER datasets. However, the directed acyclic graph (DAG) structure makes lattice LSTM challenging to train on minibatch. In addition, the Lattice LSTM and WC-LSTM only incorporate the word-level semantics into the representation of the first or last character in each word. The inside characters that the word contain are ignored. Besides, they have difficulty in dealing with the conflicts between potential words in the lexicon. This work proposes an attention- based hierarchical meta-embedding method (AHME) to incorporate lexicon knowledge into Chinese NER to alleviate the above limitations. The proposed model can incorporate the word boundary information into character representation and deal with conflicts between potential incorporated words. The experimental results on four datasets show that our method outperforms state-of-the-art baselines.
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Shuo Liu and Yinliang Zhao "Incorporating lexicon knowledge into Chinese NER using hierarchical meta-embedding", Proc. SPIE 12156, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021), 121560J (21 December 2021); https://doi.org/10.1117/12.2626480
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KEYWORDS
Performance modeling
Computer programming
Transformers
Data modeling
Neural networks
Systems modeling
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