计算机科学
词汇
自然语言处理
命名实体识别
人工智能
语音识别
模式识别(心理学)
工程类
语言学
系统工程
哲学
任务(项目管理)
作者
Ye Liu,Shaobin Huang,Rongsheng Li,Naiyu Yan,Zhenguo Du
标识
DOI:10.1016/j.ipm.2023.103290
摘要
Due to the particularity of Chinese word formation, the Chinese Named Entity Recognition (NER) task has attracted extensive attention over recent years. Recently, some researchers have tried to solve this problem by using a multimodal method combining acoustic features and text features. However, the text-speech data pairs required by the above methods are lacking in real-world scenarios, making it difficult to apply widely. To address this, we proposed a multimodal Chinese NER method called USAF, which uses synthesized acoustic features instead of actual human speech. USAF aligns text and acoustic features through unique position embeddings and uses a multi-head attention mechanism to fuse the features of the two modalities, which stably improves the performance of Chinese named entity recognition. To evaluate USAF, we implemented USAF on three Chinese NER datasets. Experimental results show that USAF witnesses a stable improvement compare to text-only methods on each dataset, and outperforms SOTA external-vocabulary-based method on two datasets. Specifically, compared to the SOTA external-vocabulary-based method, the F1 score of USAF is improved by 1.84 and 1.24 on CNERTA and Aishell3-NER, respectively.
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