Paper Info
Title
Implicit Entity Recognition, Classification and Linking in Tweets
Abstract
Linking phrases to knowledge base entities is a process known as entity linking and has already been widely explored for various content types such as tweets. A major step in entity linking is to recognize and/or classify phrases that can be disambiguated and linked to knowledge base entities, i.e., Named Entity Recognition and Classification. Unlike common entity recognition and linking systems, however, we aim to recognize, classify, and link entities which are implicitly mentioned, and hence lack a surface form, to appropriate knowledge base entries. In other words, the objective of our work is to recognize and identify core entities of a tweet when those entities are not explicitly mentioned; this process is referred to as Implicit Named Entity Recognition and Linking.
Year
DOI
Venue
2019
10.1145/3331184.3331416
Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval
Keywords
Field
DocType
implicit entity linking, implicit entity recognition and classification, semantic retrieval
Information retrieval,Computer science
Conference
ISBN
Citations 
PageRank 
978-1-4503-6172-9
0
0.34
References 
Authors
0
1
Name
Order
Citations
PageRank
Hawre Hosseini111.71