Using Embeddings for Both Entity Recognition and Linking in Tweets
Year: 2016
Type: book-chapter
Abstract: The paper describes our submissions to the task on Named Entity rEcognition and Linking in Italian Tweets (NEEL-IT) at Evalita 2016. Our approach relies on a technique of Named Entity tagging that exploits both character-level and word-level embeddings. Character-based embeddings allow learning the idiosyncrasies of the language used in tweets. Using a full-blown Named Entity tagger allows recognizing a wider range of entities than those well known by their presence in a Knowledge Base or gazetteer. Our submissions achieved first, second and fourth top official scores.
Authors: Giuseppe Attardi, Daniele Sartiano, Maria Simi, Irene Sucameli
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2575344390