Lessons learnt from the Named Entity rEcognition and Linking (NEEL) challenge series

Giuseppe Rizzo*, Bianca Pereira, Andrea Varga, Marieke Van Erp, Amparo Elizabeth Cano Basave

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review


The large number of tweets generated daily is providing decision makers with means to obtain insights into recent events around the globe in near real-time. The main barrier for extracting such insights is the impossibility of manual inspection of a diverse and dynamic amount of information. This problem has attracted the attention of industry and research communities, resulting in algorithms for the automatic extraction of semantics in tweets and linking them to machine readable resources. While a tweet is shallowly comparable to any other textual content, it hides a complex and challenging structure that requires domain-specific computational approaches for mining semantics from it. The NEEL challenge series, established in 2013, has contributed to the collection of emerging trends in the field and definition of standardised benchmark corpora for entity recognition and linking in tweets, ensuring high quality labelled data that facilitates comparisons between different approaches. This article reports the findings and lessons learnt through an analysis of specific characteristics of the created corpora, limitations, lessons learnt from the different participants and pointers for furthering the field of entity recognition and linking in tweets.

Original languageEnglish
Pages (from-to)667-700
Number of pages34
JournalSemantic Web
Issue number5
Publication statusPublished - 1 Jan 2017


  • challenge
  • disambiguation
  • evaluation
  • knowledge base
  • Microposts
  • named entity linking
  • named entity recognition


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