Learning to rank implicit entities on Twitter
作者:
Highlights:
• Introduction and systematic classification of features for identifying implicitly mentioned entities in tweets.
• The examination of features in the context of both explicit and implicit entity linking tasks.
• Qualitative and quantitative assessment of the performance of the features, individually and collectively.
• Root cause analysis for why certain types of features perform better (or worse) for the task of implicit entity linking.
摘要
•Introduction and systematic classification of features for identifying implicitly mentioned entities in tweets.•The examination of features in the context of both explicit and implicit entity linking tasks.•Qualitative and quantitative assessment of the performance of the features, individually and collectively.•Root cause analysis for why certain types of features perform better (or worse) for the task of implicit entity linking.
论文关键词:Knowledge graph,Entity linking,DBpedia,Learn to rank
论文评审过程:Received 30 July 2020, Revised 13 December 2020, Accepted 6 January 2021, Available online 20 January 2021, Version of Record 20 January 2021.
论文官网地址:https://doi.org/10.1016/j.ipm.2021.102503