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doi:10.22028/D291-38852
Titel: | DiscoGeM: A Crowdsourced Corpus of Genre-Mixed Implicit Discourse Relations |
VerfasserIn: | Scholman, Merel Cleo Johanna Dong, Tianai Yung, Frances Pikyu Demberg, Vera |
HerausgeberIn: | Calzolari, Nicoletta |
Sprache: | Englisch |
Titel: | Language Resources and Evaluation Conference, LREC 2022, 20-25 June 2022 : Palais du Pharo, Marseille, France : conference proceedings |
Seiten: | 3281-3290 |
Verlag/Plattform: | European Language Resources Association |
Erscheinungsjahr: | 2022 |
Erscheinungsort: | Paris |
Konferenzort: | Marseille, France |
Freie Schlagwörter: | discourse annotations implicit relations genre crowdsourcing label aggregation |
DDC-Sachgruppe: | 004 Informatik 400 Sprache, Linguistik |
Dokumenttyp: | Konferenzbeitrag (in einem Konferenzband / InProceedings erschienener Beitrag) |
Abstract: | We present DiscoGeM, a crowdsourced corpus of 6,505 implicit discourse relations from three genres: political speech, literature, and encyclopedic texts. Each instance was annotated by 10 crowd workers. Various label aggregation methods were explored to evaluate how to obtain a label that best captures the meaning inferred by the crowd annotators. The results show that a significant proportion of discourse relations in DiscoGeM are ambiguous and can express multiple relation senses. Probability distribution labels better capture these interpretations than single labels. Further, the results emphasize that text genre crucially affects the distribution of discourse relations, suggesting that genre should be included as a factor in automatic relation classification. We make available the newly created DiscoGeM corpus, as well as the dataset with all annotator-level labels. Both the corpus and the dataset can facilitate a multitude of applications and research purposes, for example to function as training data to improve the performance of automatic discourse relation parsers, as well as facilitate research into non-connective signals of discourse relations. |
URL der Erstveröffentlichung: | https://aclanthology.org/2022.lrec-1.351/ |
Link zu diesem Datensatz: | urn:nbn:de:bsz:291--ds-388529 hdl:20.500.11880/35059 http://dx.doi.org/10.22028/D291-38852 |
ISBN: | 979-10-95546-72-6 |
Datum des Eintrags: | 31-Jan-2023 |
Fakultät: | MI - Fakultät für Mathematik und Informatik |
Fachrichtung: | MI - Informatik |
Professur: | MI - Prof. Dr. Vera Demberg |
Sammlung: | SciDok - Der Wissenschaftsserver der Universität des Saarlandes |
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