Deep recurrent neural network for geographical entities disambiguation on social media data
作者：
Highlights：
• A challenge in Natural Language Processing is the disambiguation of polysemic words.
• Named entities are also polysemic, so their disambiguation is required.
• Disambiguation methods highly depend on linguistic resources.
• We propose a deep recurrent neural network that does not use any linguistic resourse.
• The results show that our model can disambiguate the target entity.
摘要
•A challenge in Natural Language Processing is the disambiguation of polysemic words.•Named entities are also polysemic, so their disambiguation is required.•Disambiguation methods highly depend on linguistic resources.•We propose a deep recurrent neural network that does not use any linguistic resourse.•The results show that our model can disambiguate the target entity.
论文关键词：Word sense disambiguation,Deep learning,Recurrent neural networks
论文评审过程：Received 8 June 2018, Revised 4 February 2019, Accepted 23 February 2019, Available online 2 March 2019, Version of Record 21 March 2019.
论文官网地址：https://doi.org/10.1016/j.knosys.2019.02.030
