TY - GEN
T1 - DLocRL
T2 - 2019 World Wide Web Conference, WWW 2019
AU - Xu, Canwen
AU - Pei, Jiaxin
AU - Li, Jing
AU - Li, Chenliang
AU - Luo, Xiangyang
AU - Ji, Donghong
N1 - Publisher Copyright:
© 2019 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC-BY 4.0 License.
PY - 2019/5/13
Y1 - 2019/5/13
N2 - In recent years, with the prevalence of social media and smart devices, people causally reveal their locations such as shops, hotels, and restaurants in their tweets. Recognizing and linking such fine-grained location mentions to well-defined location profiles are beneficial for retrieval and recommendation systems. In this paper, we propose DLocRL, a new deep learning pipeline for fine-grained location recognition and linking in tweets, and verify its effectiveness on a real-world Twitter dataset.
AB - In recent years, with the prevalence of social media and smart devices, people causally reveal their locations such as shops, hotels, and restaurants in their tweets. Recognizing and linking such fine-grained location mentions to well-defined location profiles are beneficial for retrieval and recommendation systems. In this paper, we propose DLocRL, a new deep learning pipeline for fine-grained location recognition and linking in tweets, and verify its effectiveness on a real-world Twitter dataset.
KW - Entity linking
KW - Named entity recognition
KW - POI recognition and linking
KW - Social media content analysis
UR - https://www.scopus.com/pages/publications/85066899758
U2 - 10.1145/3308558.3313491
DO - 10.1145/3308558.3313491
M3 - 会议稿件
AN - SCOPUS:85066899758
T3 - The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019
SP - 3391
EP - 3397
BT - The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019
PB - Association for Computing Machinery, Inc
Y2 - 13 May 2019 through 17 May 2019
ER -