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Hi everyone,</div>
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<span style="margin: 0px; background-color: white">Willy will be leading the discussion of the following paper next Thursday via Zoom (</span><span style="margin: 0px; background-color: white"><a href="https://urldefense.com/v3/__https://osu.zoom.us/j/94712017994?pwd=cVdNUGRSMkMyU2R0dTZaZ2xnSmxjUT09__;!!KGKeukY!hYGiY_UAuKLEYNU4jiPcQ5s8lVvpAdOrFRRJXO5SsGDNjowTpFbEysaH8ahMkX8gyR3TY9iOdw$" target="_blank" rel="noopener noreferrer" data-auth="NotApplicable" style="margin: 0px">https://osu.zoom.us/j/94712017994?pwd=cVdNUGRSMkMyU2R0dTZaZ2xnSmxjUT09</a>)</span><span style="margin: 0px; background-color: white">:</span></div>
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<p><span style="font-family: Calibri, Arial, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);"><b>A Crowdsourced Corpus of Multiple Judgments and Disagreement on Anaphoric Interpretation</b></span></p>
<p><span style="font-family: Calibri, Arial, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);"><b><a href="https://www.aclweb.org/anthology/N19-1176.pdf" id="LPlnk">https://www.aclweb.org/anthology/N19-1176.pdf</a><br>
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<span style="text-align: left; display: inline !important; font-family: Calibri, Arial, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">We present a corpus of anaphoric information (coreference) crowdsourced through a game-with-a-purpose. The
corpus, containing annotations for about 108,000 markables, is one of the largest corpora for coreference for English, and one of the largest crowdsourced NLP corpora, but its main feature is the large number of judgments per markable: 20 on average, and over
2.2M in total. This characteristic makes the corpus a unique resource for the study of disagreements on anaphoric interpretation. A second distinctive feature is its rich annotation scheme, covering singletons, expletives, and split-antecedent plurals. Finally,
the corpus also comes with labels inferred using a recently proposed probabilistic model of annotation for coreference. The labels are of high quality and make it possible to successfully train a state of the art coreference resolver, including training on
singletons and non-referring expressions. The annotation model can also result in more than one label, or no label, being proposed for a markable, thus serving as a baseline method for automatically identifying ambiguous markables. A preliminary analysis of
the results is presented.</span></div>
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<span style="font-family:"Lucida Sans Unicode","Lucida Grande",sans-serif; font-size:10pt">=================</span></div>
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<span style="font-size:10pt"></span><span style="font-size:11pt"></span><span style="font-family:"Lucida Sans Unicode","Lucida Grande",sans-serif; font-size:10pt"><b>Byung-Doh Oh</b> (he/him/his)</span></div>
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<span style="font-size:10pt"></span><span style="font-size:11pt"></span><span style="font-family:"Lucida Sans Unicode","Lucida Grande",sans-serif; font-size:10pt">Ph.D. Student</span></div>
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<span style="font-size:10pt"></span><span style="font-size:11pt"></span><span style="font-family:"Lucida Sans Unicode","Lucida Grande",sans-serif; font-size:10pt">Department of Linguistics</span></div>
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<span style="font-size:10pt"></span><span style="font-size:11pt"></span><span style="font-family:"Lucida Sans Unicode","Lucida Grande",sans-serif; font-size:10pt">The Ohio State University</span></div>
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<span style="font-size:10pt"></span><span style="font-size:11pt"></span><a href="https://byungdoh.github.io"><span style="font-family:"Lucida Sans Unicode","Lucida Grande",sans-serif; font-size:10pt">https://byungdoh.github.io</span></a><br>
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