Basile, Pierpaolo ORCID: 0000-0002-0545-1105 and Caputo, Annalina ORCID: 0000-0002-7144-8545 (2017) Entity linking for Tweets. Encyclopedia with Semantic Computing and Robotic Intelligence, 1 (1). pp. 1-9. ISSN 2529-7376
Abstract
Named Entity Linking (NEL) is the task of semantically annotating entity mentions in a portion of text with links to a knowledge base. The automatic annotation, which requires the recognition and disambiguation of the entity mention, usually exploits
contextual clues like the context of usage and the coherence with respect to other entities. In Twitter, the limits of 140 characters
originates very short and noisy text messages that pose new challenges to the entity linking task. We propose an overview of NEL
methods focusing on approaches specifically developed to deal with short messages, like tweets. NEL is a fundamental task for
the extraction and annotation of concepts in tweets, which is necessary for making the Twitter’s huge amount of interconnected
user-generated contents machine readable and enable the intelligent information access.
Metadata
Item Type: | Article (Published) |
---|---|
Refereed: | Yes |
Additional Information: | Article Number 16300202 |
Uncontrolled Keywords: | Entity linking; Twitter |
Subjects: | Computer Science > Artificial intelligence Computer Science > Computational linguistics |
DCU Faculties and Centres: | DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing |
Publisher: | World Scientific Publishing Company |
Official URL: | https://dx.doi.org/10.1142/S2425038416300202 |
Copyright Information: | © 2017 The Authors |
Use License: | This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License |
Funders: | “Multilingual Entity Liking” funded by the Apulia Region under the program FutureInResearch, ADAPT Centre for Digital Content Technology, which is funded under the Science Foundation Ireland Research Centres Programme (Grant 13/RC/2106) and is co-funded under the European Regional Development Fund |
ID Code: | 25690 |
Deposited On: | 26 Mar 2021 12:05 by Annalina Caputo . Last Modified 26 Mar 2021 12:05 |
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