Dataset Open Access
Rijhwani, Shruti; Preoțiuc-Pietro, Daniel
<?xml version='1.0' encoding='utf-8'?> <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:adms="http://www.w3.org/ns/adms#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dct="http://purl.org/dc/terms/" xmlns:dctype="http://purl.org/dc/dcmitype/" xmlns:dcat="http://www.w3.org/ns/dcat#" xmlns:duv="http://www.w3.org/ns/duv#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:frapo="http://purl.org/cerif/frapo/" xmlns:geo="http://www.w3.org/2003/01/geo/wgs84_pos#" xmlns:gsp="http://www.opengis.net/ont/geosparql#" xmlns:locn="http://www.w3.org/ns/locn#" xmlns:org="http://www.w3.org/ns/org#" xmlns:owl="http://www.w3.org/2002/07/owl#" xmlns:prov="http://www.w3.org/ns/prov#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns:schema="http://schema.org/" xmlns:skos="http://www.w3.org/2004/02/skos/core#" xmlns:vcard="http://www.w3.org/2006/vcard/ns#" xmlns:wdrs="http://www.w3.org/2007/05/powder-s#"> <rdf:Description rdf:about="https://doi.org/10.5281/zenodo.3899040"> <rdf:type rdf:resource="http://www.w3.org/ns/dcat#Dataset"/> <dct:type rdf:resource="http://purl.org/dc/dcmitype/Dataset"/> <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#anyURI">https://doi.org/10.5281/zenodo.3899040</dct:identifier> <foaf:page rdf:resource="https://doi.org/10.5281/zenodo.3899040"/> <dct:creator> <rdf:Description> <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/> <foaf:name>Rijhwani, Shruti</foaf:name> <foaf:givenName>Shruti</foaf:givenName> <foaf:familyName>Rijhwani</foaf:familyName> <org:memberOf> <foaf:Organization> <foaf:name>Bloomberg</foaf:name> </foaf:Organization> </org:memberOf> </rdf:Description> </dct:creator> <dct:creator> <rdf:Description> <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/> <foaf:name>Preoțiuc-Pietro, Daniel</foaf:name> <foaf:givenName>Daniel</foaf:givenName> <foaf:familyName>Preoțiuc-Pietro</foaf:familyName> <org:memberOf> <foaf:Organization> <foaf:name>Bloomberg</foaf:name> </foaf:Organization> </org:memberOf> </rdf:Description> </dct:creator> <dct:title>Temporally-Informed Analysis of Named Entity Recognition</dct:title> <dct:publisher> <foaf:Agent> <foaf:name>Zenodo</foaf:name> </foaf:Agent> </dct:publisher> <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2020</dct:issued> <dcat:keyword>named entity recognition</dcat:keyword> <dcat:keyword>twitter</dcat:keyword> <dcat:keyword>ner</dcat:keyword> <dcat:keyword>twitter ner</dcat:keyword> <dcat:keyword>tweets</dcat:keyword> <dcat:keyword>temporal analysis</dcat:keyword> <dcat:keyword>information extraction</dcat:keyword> <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2020-06-17</dct:issued> <dct:language rdf:resource="http://publications.europa.eu/resource/authority/language/ENG"/> <owl:sameAs rdf:resource="https://zenodo.org/record/3899040"/> <adms:identifier> <adms:Identifier> <skos:notation rdf:datatype="http://www.w3.org/2001/XMLSchema#anyURI">https://zenodo.org/record/3899040</skos:notation> <adms:schemeAgency>url</adms:schemeAgency> </adms:Identifier> </adms:identifier> <dct:isVersionOf rdf:resource="https://doi.org/10.5281/zenodo.3899039"/> <dct:description><p>This repository contains the data set developed for the paper:</p> <p>&ldquo;Shruti Rijhwani and Daniel Preoțiuc-Pietro. <em>Temporally-Informed Analysis of Named Entity Recognition.</em> In Proceedings of the Association for Computational Linguistics (ACL). 2020.&rdquo;</p> <p>It includes 12,000 tweets annotated for the named entity recognition task. The tweets are uniformly distributed over the years 2014-2019, with 2,000 tweets from each year. The goal is to have a temporally diverse corpus to account for data drift over time when building NER models.</p> <p>The entity types annotated are locations (LOC), persons (PER) and organizations (ORG). The tweets are preprocessed to replace usernames and URLs with a unique token. Hashtags are left intact and can be annotated as named entities.</p> <p><strong>Format</strong></p> <p>The repository contains the annotations in JSON format.</p> <p>Each year-wise file has the tweet IDs along with token-level annotations. The Public Twitter Search API (<a href="https://developer.twitter.com/en/docs/tweets/search">https://developer.twitter.com/en/docs/tweets/search</a>) can be used extract the text for the tweet corresponding to the tweet IDs.</p> <p><strong>Data Splits</strong></p> <p>Typically, NER models are trained and evaluated on annotations available at the model building time, but are used to make predictions on data from a future time period. This setup makes the model susceptible to temporal data drift, leading to lower performance on future data as compared to the test set.</p> <p>To examine this effect, we use tweets from the years 2014-2018 as the training set and random splits of the 2019 tweets as the development and test sets. These splits simulate the scenario of making predictions on data from a future time period.</p> <p>The development and test splits are provided in the JSON format.</p> <p><strong>Use</strong></p> <p>Please cite the data set and the accompanying paper if you found the resources in this repository useful.</p></dct:description> <dct:accessRights rdf:resource="http://publications.europa.eu/resource/authority/access-right/PUBLIC"/> <dct:accessRights> <dct:RightsStatement rdf:about="info:eu-repo/semantics/openAccess"> <rdfs:label>Open Access</rdfs:label> </dct:RightsStatement> </dct:accessRights> <dcat:distribution> <dcat:Distribution> <dct:license rdf:resource="https://creativecommons.org/licenses/by/4.0/legalcode"/> <dcat:accessURL rdf:resource="https://doi.org/10.5281/zenodo.3899040"/> </dcat:Distribution> </dcat:distribution> <dcat:distribution> <dcat:Distribution> <dcat:accessURL>https://doi.org/10.5281/zenodo.3899040</dcat:accessURL> <dcat:byteSize>185283</dcat:byteSize> <dcat:downloadURL>https://zenodo.org/record/3899040/files/temporal-ner-twitter-corpus.zip</dcat:downloadURL> <dcat:mediaType>application/zip</dcat:mediaType> </dcat:Distribution> </dcat:distribution> </rdf:Description> </rdf:RDF>
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