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Although several datasets annotated for anaphoric reference/coreferenceexist, even the largest such datasets have limitations in terms of size, rangeof domains, coverage of anaphoric phenomena, and size of documents included.Yet, the approaches proposed to scale up anaphoric annotation haven't so farresulted in datasets overcoming these limitations. In this paper, we introducea new release of a corpus for anaphoric reference labelled via agame-with-a-purpose. This new release is comparable in size to the largestexisting corpora for anaphoric reference due in part to substantial activity bythe players, in part thanks to the use of a new resolve-and-aggregate paradigmto 'complete' markable annotations through the combination of an anaphoricresolver and an aggregation method for anaphoric reference. The proposed methodcould be adopted to greatly speed up annotation time in other projectsinvolving games-with-a-purpose. In addition, the corpus covers genres for whichno comparable size datasets exist (Fiction and Wikipedia); it covers singletonsand non-referring expressions; and it includes a substantial number of longdocuments (> 2K in length).

Aggregating Crowdsourced and Automatic Judgments to Scale Up a Corpus of Anaphoric Reference for Fiction and Wikipedia Texts