# On the Use of Parsing for Named Entity Recognition
## 2 Named Entity Recognition
### Shared Tasks and Data Sets for NER
* BSNLP ([[http://bsnlp.cs.helsinki.fi/bsnlp-2019/shared_task.html](http://bsnlp.cs.helsinki.fi/bsnlp-2019/shared_task.html)]([http://bsnlp.cs.helsinki.fi/bsnlp-2019/shared_task.html](http://bsnlp.cs.helsinki.fi/bsnlp-2019/shared_task.html))), the series of Balto-Slavic Natural Language Processing shared tasks on multilingual NER. The data set of 2017 [80] comprised texts in Croatian, Czech, Polish, Russian, Slovak, Slovene and Ukrainian; while the 2019 edition [81] was dedicated to Bulgarian, Czech, Polish, and Russian. * VNER 2018 ([[https://vlsp.org.vn/vlsp2018/eval/ner](https://vlsp.org.vn/vlsp2018/eval/ner)]([https://vlsp.org.vn/vlsp2018/eval/ner](https://vlsp.org.vn/vlsp2018/eval/ner))), VLSP 2018 Shared Task on NER for Vietnamese [82]. Texts in the data set were collected from online newspaper websites. * NSURL-2019 ([[http://nsurl.org/tasks/task-7-named-entity-recognition-ner-for-farsi/](http://nsurl.org/tasks/task-7-named-entity-recognition-ner-for-farsi/)]([http://nsurl.org/tasks/task-7-named-entity-recognition-ner-for-farsi/](http://nsurl.org/tasks/task-7-named-entity-recognition-ner-for-farsi/))), the First International Workshop on NLP Solutions for Under Resourced Languages, included among its shared tasks one dedicated to NER for Farsi (Task 7) [83]. * IberLEF ([[https://sites.google.com/view/iberlef2020](https://sites.google.com/view/iberlef2020)]([https://sites.google.com/view/iberlef2020](https://sites.google.com/view/iberlef2020))), the series of Iberian Languages Evaluation Forum workshops formerly known as IberEval (Workshops on Evaluation of Human Language Technologies for Iberian Languages), and dedicated to NLP in Iberian Languages, has always paid attention to entity recognition and processing. At first they proposed tasks on abbreviation recognition and resolution in the Spanish biomedical domain [84; 85] and, after that, they decided to include tracks on Portuguese NER [86], Spanish NER [87] and Spanish eHealth Knowledge Discovery [88]. In addition, the following are some of the data sets that are routinely used by NER researchers for evaluation purposes:  
* GENIA ([[http://www.geniaproject.org/genia-corpus](http://www.geniaproject.org/genia-corpus)]([http://www.geniaproject.org/genia-corpus](http://www.geniaproject.org/genia-corpus))) [89], a collection created to support the development and evaluation of information extraction and text mining systems for the molecular biology domain. It contains subcorpora annotated with PoS, constituency (phrase structure), terms, events, relations, and coreferences. * OntoNotes ([[https://www.gabormelli.com/RKB/OntoNotes_Corpus](https://www.gabormelli.com/RKB/OntoNotes_Corpus)]([https://www.gabormelli.com/RKB/OntoNotes_Corpus](https://www.gabormelli.com/RKB/OntoNotes_Corpus))) [90], a data set on English, Chinese, and Arabic resulting from the project with the same name. Apart from named entities from several domains (weblogs, news, talk shows, broadcast, Usenet newsgroups, and conversational telephone speech) it also contains structural information (constituency trees and predicate argument structure) and shallow semantics (word sense linked to an ontology and coreference). There are five releases of OntoNotes, from 1.0 to 5.0. * CHEMDNER ([[https://biocreative.bioinformatics.udel.edu/resources/biocreative-iv/chemdner-corpus/](https://biocreative.bioinformatics.udel.edu/resources/biocreative-iv/chemdner-corpus/)]([https://biocreative.bioinformatics.udel.edu/resources/biocreative-iv/chemdner-corpus/](https://biocreative.bioinformatics.udel.edu/resources/biocreative-iv/chemdner-corpus/))) [91], a corpus for evaluating text mining and information extraction systems applied to the chemical domain. * NCBI disease corpus ([[https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEA](https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEA)]([https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEA](https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEA)) SE/) [92], a collection of PubMed abstracts fully annotated at both the mention and concept levels.