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Lessons Learnt from the Named Entity rEcognition and Linking (NEEL) Challenge Series

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Giuseppe Rizzo, Biana Pereira, Andra Varga, Marieke van Erp, Amparo Elizabeth Cano Basave

Presented on Wednesday 10 October at the 17th International Semantic Web Conference (ISWC 2018)

Paper: http://www.semantic-web-journal.net/content/lessons-learnt-named-entity-recognition-and-linking-neel-challenge-series

Conference: http://iswc2018.semanticweb.org/

Published in: Technology
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Lessons Learnt from the Named Entity rEcognition and Linking (NEEL) Challenge Series

  1. 1. 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 By Piet Mondrian - Gemeentemuseum Den Haag, Public Domain, https://commons.wikimedia.org/w/index.php?curid=37614350
  2. 2. NEEL Challenge Overview • Microposts are challenging because: • brevity (140 characters) • (domain specific) abbreviations and typos • ‘grammar free’ • The NEEL challenge aims to explore new approaches to foster research into novel, more accurate entity recognition and linking approaches tailored to Microposts • NEEL ran from 2013 - 2016
  3. 3. NEEL Evolution • 2013: Information Extraction • named entity recognition (4 types) • 2014: Named Entity Extraction and Linking (NEEL) • named entity linking to DBpedia 3.9 • 2015: Named Entity rEcognition and Linking (NEEL) • named entity recognition (7 types) and linking to DBpedia 2014 • 2016: Named Entity rEcognition and Linking (NEEL) • named entity recognition (7 types) and linking to DBpedia 2015-04, NIL clustering Image source: https://c1.staticflickr.com/8/7020/6405801675_efd6d09977_b.jpg
  4. 4. Cross-domain task • Named Entity and Event Linking is a shared task in NLP and Semantic Web • Machine Learning approaches need data • Data curation is expensive and hard • Knowledge bases can reduce some of the data bottleneck • Resulting in hybrid approaches
  5. 5. Typical Entity Linking Workflow
  6. 6. Evaluating Entity Linking • end-to-end: evaluates a system on the aggregated output of all steps • error propagation harms results • step-by-step: robust benchmark that evaluates each step of the process individually • time consuming to set up • penalises systems that do not follow standard workflow • partial end-to-end: evaluates particular steps in the process individually e.g. NER, NIL & Linking
  7. 7. Named Entity Recognition and Linking challenges since 2013 Characteris tic TAC-KBP ERD SemEval W-NUT NEEL 2014 2015 2016 2014 2015 2015 2016 2017 2013 2014 2015 2016 Text newswire web sites discussion forum posts web sites search queries technical manuals reports formal discussion tweets tweets Reddit YouTube StackExchange tweets Kowledge Base Wikipedia Freebase Freebase Babelnet none none none none DBpedia Entity given by Type given by KB given by KB given by Type given by Type Evaluation file API file file file API file partial end-to-end end-to- end end-to-end end-to-end end-to-end partial end-to-end Target conference TAC SIGIR NAACL-HLT ACL-IJNLP COLING EMNLP WWW
  8. 8. Named Entity Recognition and Linking challenges since 2013 Characteris tic TAC-KBP ERD SemEval W-NUT NEEL 2014 2015 2016 2014 2015 2015 2016 2017 2013 2014 2015 2016 Text newswire web sites discussion forum posts web sites search queries technical manuals reports formal discussion tweets tweets Reddit YouTube StackExchange tweets Kowledge Base Wikipedia Freebase Freebase Babelnet none none none none DBpedia Entity given by Type given by KB given by KB given by Type given by Type Evaluation file API file file file API file partial end-to-end end-to- end end-to-end end-to-end end-to-end partial end-to-end Target conference TAC SIGIR NAACL-HLT ACL-IJNLP COLING EMNLP WWW
  9. 9. Named Entity Recognition and Linking challenges since 2013 Characteris tic TAC-KBP ERD SemEval W-NUT NEEL 2014 2015 2016 2014 2015 2015 2016 2017 2013 2014 2015 2016 Text newswire web sites discussion forum posts web sites search queries technical manuals reports formal discussion tweets tweets Reddit YouTube StackExchange tweets Kowledge Base Wikipedia Freebase Freebase Babelnet none none none none DBpedia Entity given by Type given by KB given by KB given by Type given by Type Evaluation file API file file file API file partial end-to-end end-to- end end-to-end end-to-end end-to-end partial end-to-end Target conference TAC SIGIR NAACL-HLT ACL-IJNLP COLING EMNLP WWW
  10. 10. Named Entity Recognition and Linking challenges since 2013 Characteris tic TAC-KBP ERD SemEval W-NUT NEEL 2014 2015 2016 2014 2015 2015 2016 2017 2013 2014 2015 2016 Text newswire web sites discussion forum posts web sites search queries technical manuals reports formal discussion tweets tweets Reddit YouTube StackExchange tweets Kowledge Base Wikipedia Freebase Freebase Babelnet none none none none DBpedia Entity given by Type given by KB given by KB given by Type given by Type Evaluation file API file file file API file partial end-to-end end-to- end end-to-end end-to-end end-to-end partial end-to-end Target conference TAC SIGIR NAACL-HLT ACL-IJNLP COLING EMNLP WWW
  11. 11. NEEL Datasets Image source: https://www.maxpixel.net/Word-Data-Data-Deluge-Binary-System-Binary-Dataset-2728117 • 2013: 4,265 tweets, end of 2010, start of 2011. No explicit hashtag search, 66% train, 33% test. • 2014: 3,505 tweets, 15 July 2011 - 15 August 2011. First Story Detection algorithm to identify tweet clusters representing events, 70% train, 30% test. • 2015: 6,025 tweets, extension of 2014 dataset including tweets from 2013 and November 2014. Train: 2014 dataset, 8% development, 34% test. • 2016: 9,289 tweets, extension of 2014 & 2015 datasets via selection of hashtags. 65% train (2015 datset), 1% development and 34% test.
  12. 12. NEEL Datasets (ctd) • Entity types are not distributed equally • Difficult to balance entity types over different dataset slices • Confusability: a measure of the number of surface forms an entity can have (i.e. how many different ‘terms’ can refer to the same entity) • Dominance: a measure of the number of resources can be associated with a single surface form (i.e. how many entities share the same ‘name’) 2013 2016 Confusability Dominance
  13. 13. Results • NEEL Challenge more difficult every year (from 4 entity types to 7 + linking + NIL clustering) • Systems more complex every year • 2016 task more difficult probably due to domain specificity of test dataset (US Primary Elections and Star Wars) Precision Recall F1 2013 0.764 0.604 0.67 2014 0.771 0.642 0.701 Tagging Clustering Linking Overall 2015 0.807 0.84 0.762 0.8067 2016 0.473 0.641 0.501 0.5486
  14. 14. Emerging Trends • Tweet normalisation is common • Use of KBs for mention detection and typing • End-to-end systems and pruning for candidate selection • Hierarchical clustering for aggregating mentions of the same entity/event • Decrease in the use of off-the-shelf systems (which were popular in the first editions)
  15. 15. Lessons Learnt • Creating balanced challenge datasets is hard! • You are invited to expand and improve our datasets! • The datasets are available for evaluation of new systems: http:// microposts2016.seas.upenn.edu/challenge.html • NEEL provides an opportunity to compare results against other systems • Multilingual or other language challenges? (2016 also had an Italian variant) • New popular micropost platforms require different analyses
  16. 16. Acknowledgments: Image source: https://upload.wikimedia.org/wikipedia/commons/d/de/The_Canadian_field-naturalist_%281983%29_%2819897979884%29.jpg
  17. 17. Are you a Master’s or PhD student? Do you want to learn how to do this type of research yourself? Join us in Italy next summer! http://semanticwebsummerschool.org

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