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Temporally-Informed Analysis of Named Entity Recognition

Rijhwani, Shruti; Preoțiuc-Pietro, Daniel


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{
  "inLanguage": {
    "alternateName": "eng", 
    "@type": "Language", 
    "name": "English"
  }, 
  "description": "<p>This repository contains the data set developed for the paper:</p>\n\n<p>&ldquo;Shruti Rijhwani and Daniel Preo\u021biuc-Pietro. <em>Temporally-Informed Analysis of Named Entity Recognition.</em> In Proceedings of the Association for Computational Linguistics (ACL). 2020.&rdquo;</p>\n\n<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>\n\n<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>\n\n<p><strong>Format</strong></p>\n\n<p>The repository contains the annotations in JSON format.</p>\n\n<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>\n\n<p><strong>Data Splits</strong></p>\n\n<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>\n\n<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>\n\n<p>The development and test splits are provided in the JSON format.</p>\n\n<p><strong>Use</strong></p>\n\n<p>Please cite the data set and the accompanying paper if you found the resources in this repository useful.</p>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "Bloomberg", 
      "@type": "Person", 
      "name": "Rijhwani, Shruti"
    }, 
    {
      "affiliation": "Bloomberg", 
      "@type": "Person", 
      "name": "Preo\u021biuc-Pietro, Daniel"
    }
  ], 
  "url": "https://zenodo.org/record/3899040", 
  "datePublished": "2020-06-17", 
  "@type": "Dataset", 
  "keywords": [
    "named entity recognition", 
    "twitter", 
    "ner", 
    "twitter ner", 
    "tweets", 
    "temporal analysis", 
    "information extraction"
  ], 
  "@context": "https://schema.org/", 
  "distribution": [
    {
      "contentUrl": "https://zenodo.org/api/files/a2fe4f96-2590-4c4f-a549-6b701221d940/temporal-ner-twitter-corpus.zip", 
      "encodingFormat": "zip", 
      "@type": "DataDownload"
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  "identifier": "https://doi.org/10.5281/zenodo.3899040", 
  "@id": "https://doi.org/10.5281/zenodo.3899040", 
  "workFeatured": {
    "url": "https://acl2020.org", 
    "alternateName": "ACL2020", 
    "@type": "Event", 
    "name": "The 58th Annual Meeting of the Association for Computational Linguistics"
  }, 
  "name": "Temporally-Informed Analysis of Named Entity Recognition"
}
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