Name
Abstract | ||
|---|---|---|
The systematic linking of explicitly-observed phrases within a document to entities of a knowledge base has already been explored in a process known as entity linking. The objective of this paper, however, is to identify and entity link those entities that are not mentioned but are implied within a document, more specifically within a tweet. This process is referred to as implicit entity linking. Unlike prior work that build a representation for each entity based on its related content in the knowledge base, we propose to perform implicit entity linking by determining how a tweet is related to user-generated content posted online and as such indirectly perform entity linking. We formulate this problem as an ad-hoc document retrieval process where the input query is the tweet, which needs to be implicitly linked and the document space is the set of user-generated content related to the entities of the knowledge base. We systematically compare our work with the state-of-the-art baseline and show that our method is able to provide statistically significant improvements.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.5555/3382225.3382294 | ASONAM '18: International Conference on Advances in Social Networks Analysis and Mining
Barcelona
Spain
August, 2018 |
Keywords | Field | DocType |
user-generated content,knowledge base,implicit entity linking,systematic linking,ad-hoc document retrieval process,tweet | Entity linking,Information retrieval,Computer science,Artificial intelligence,Document retrieval,Knowledge base,Machine learning | Conference |
ISSN | ISBN | Citations |
2473-9928 | 978-1-5386-6051-5 | 0 |
PageRank | References | Authors |
0.34 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hawre Hosseini | 1 | 1 | 1.71 |
| Tam T. Nguyen | 2 | 78 | 6.79 |
| Ebrahim Bagheri | 3 | 1185 | 99.20 |