Maris Camilleri


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The ARRAU 3.0 Corpus
Massimo Poesio | Maris Camilleri | Paloma Carretero Garcia | Juntao Yu | Mark-Christoph Müller
Proceedings of the 5th Workshop on Computational Approaches to Discourse (CODI 2024)

The ARRAU corpus is an anaphorically annotated corpus designed to cover a wide variety of aspects of anaphoric reference in a variety of genres, including both written text and spoken language. The objective of this annotation project is to push forward the state of the art in anaphoric annotation, by overcoming the limitations of current annotation practice and the scope of current models of anaphoric interpretation, which in turn may reveal other issues. The resulting corpus is still therefore very much a work in progress almost twenty years after the project started. In this paper, we discuss the issues identified with the coding scheme used for the previous release, ARRAU 2, and through the use of this corpus for three shared tasks; the proposed solutions to these issues; and the resulting corpus, ARRAU 3.


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Aggregating Crowdsourced and Automatic Judgments to Scale Up a Corpus of Anaphoric Reference for Fiction and Wikipedia Texts
Juntao Yu | Silviu Paun | Maris Camilleri | Paloma Garcia | Jon Chamberlain | Udo Kruschwitz | Massimo Poesio
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

Although several datasets annotated for anaphoric reference / coreference exist, even the largest such datasets have limitations in term of size, range of domains, coverage of anaphoric phenomena, and size of documents included. Yet, the approaches proposed to scale up anaphoric annotation haven’t so far resulted in datasets overcoming these limitations. In this paper, we introduce a new release of a corpus for anaphoric reference labelled via a game-with-a-purpose. This new release is comparable in size to the largest existing corpora for anaphoric reference due in part to substantial activity by the players, in part thanks to the use of a new resolve-and-aggregate paradigm to ‘complete’ markable annotations through the combination of an anaphoric resolver and an aggregation method for anaphoric reference. The proposed method could be adopted to greatly speed up annotation time in other projects involving games-with-a-purpose. In addition, the corpus covers genres for which no comparable size datasets exist (Fiction and Wikipedia); it covers singletons and non-referring expressions; and it includes a substantial number of long documents ( 2K in length).