GeoTPE: A neural network model for geographical topic phrases extraction from literature based on BERT enhanced with relative position embedding

Author:

Li WeirongORCID,Sun KaiORCID,Zhu Yunqiang,Ding FangyuORCID,Hu Lei,Dai Xiaoliang,Song Jia,Yang Jie,Qian Lang,Wang ShuORCID

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference61 articles.

1. Auer, Sören, Lehmann, Jens, & Hellmann, Sebastian. (2009). Linkedgeodata: Adding a spatial dimension to the web of data. In the Semantic Web-ISWC 2009: 8th International Semantic Web Conference, ISWC 2009, Chantilly, VA, USA, October 25-29, 2009.

2. Awajan, Arafat Atwi. (2014). Unsupervised approach for automatic keyword extraction from Arabic documents. In the Proceedings of the 26th Conference on Computational Linguistics and Speech Processing (ROCLING 2014).

3. Bennani-Smires, Kamil, Musat, Claudiu, Hossmann, Andreea, Baeriswyl, Michael, & Jaggi, Martin. (2018). Simple unsupervised keyphrase extraction using sentence embeddings. arxiv, preprint arXiv:.04470.

4. Boudin, Florian. (2018). Unsupervised keyphrase extraction with multipartite graphs. arxiv, preprint arXiv:.08721.

5. Bougouin, Adrien, Boudin, Florian, & Daille, Béatrice. (2013). Topicrank: Graph-based topic ranking for keyphrase extraction. In the International joint conference on natural language processing (IJCNLP) (pp. 543-551).

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