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Temporally-Informed Analysis of Named Entity Recognition

Rijhwani, Shruti; Preoțiuc-Pietro, Daniel


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    <dct:title>Temporally-Informed Analysis of Named Entity Recognition</dct:title>
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    <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>
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    <dct:description>&lt;p&gt;This repository contains the data set developed for the paper:&lt;/p&gt; &lt;p&gt;&amp;ldquo;Shruti Rijhwani and Daniel Preoțiuc-Pietro. &lt;em&gt;Temporally-Informed Analysis of Named Entity Recognition.&lt;/em&gt; In Proceedings of the Association for Computational Linguistics (ACL). 2020.&amp;rdquo;&lt;/p&gt; &lt;p&gt;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.&lt;/p&gt; &lt;p&gt;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.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Format&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;The repository contains the annotations in JSON format.&lt;/p&gt; &lt;p&gt;Each year-wise file has the tweet IDs along with token-level annotations. The Public Twitter Search API (&lt;a href="https://developer.twitter.com/en/docs/tweets/search"&gt;https://developer.twitter.com/en/docs/tweets/search&lt;/a&gt;) can be used extract the text for the tweet corresponding to the tweet IDs.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Data Splits&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;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.&lt;/p&gt; &lt;p&gt;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.&lt;/p&gt; &lt;p&gt;The development and test splits are provided in the JSON format.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Use&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;Please cite the data set and the accompanying paper if you found the resources in this repository useful.&lt;/p&gt;</dct:description>
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