A Synonym Mining Algorithm Based on Pair-wise Character Embedding andNoisy Robust Learning

Synonym mining is an important task in natural language processing. In order to construct large-scale training corpus, existing studies extract synonym seeds using distant supervision and click graph filtering, which inevitably introduce noisy labels, thus affecting the training of high-quality synonym mining models. In addition, due to the few-shot and domain-distribution-shift property of most entity words, and the inconsistency between the training objective of the pre-trained word embeddings and the synonym mining task, it is difficult for the pre-trained word embeddings in the synonym mining task to produce high-quality entity semantic representations. To address these two issues, this paper proposes a synonym mining model that utilizes pair-wise character embeddings and a noise robust learning framework. The model uses pre-trained pair-wise character embeddings to enhance the entity semantic representations, estimate true label distribution and generate pseudo-labels through a joint optimization process. We want to improve the representation ability and robustness of the model through these improvements. Finally, we use WordNet to analyze and filter noisy datasets and conduct the experiments on synonym datasets of different sizes and domains. The experimental results show that the proposed synonym mining model improves the synonym set-instance classification and set generation performances compared to competitive benchmark methods under different data distribution and noise ratios. © 2023 Science Press. All rights reserved.