Phrase embedding learning from internal and external information based on autoencoder
作者：
Highlights：
• We propose an autoencoder-based method to generate phrase embedding.
• The method uses full connected network, LSTM and attention to get phrase embedding.
• The method can utilize the external and internal contextual information of phrases.
• The method can learn the order and semantic information of component words.
• The proposed method performs best on phrase similarity and classification tasks.
摘要
•We propose an autoencoder-based method to generate phrase embedding.•The method uses full connected network, LSTM and attention to get phrase embedding.•The method can utilize the external and internal contextual information of phrases.•The method can learn the order and semantic information of component words.•The proposed method performs best on phrase similarity and classification tasks.
论文关键词：Phrase embedding,Aautoencoder,LSTM,Attention mechanism
论文评审过程：Received 1 March 2020, Revised 17 September 2020, Accepted 20 October 2020, Available online 4 November 2020, Version of Record 4 November 2020.
论文官网地址：https://doi.org/10.1016/j.ipm.2020.102422
