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    <abstract>Recognizing the internal structure of events is a challenging language processing task of great importance for text understanding. We present a supervised model for automatically identifying when one event is a subevent of another. Building on prior work, we introduce several novel features, in particular discourse and narrative features, that significantly improve upon prior state-of-the-art performance. Error analysis further demonstrates the utility of these features. We evaluate our model on the only two annotated corpora with event hierarchies: HiEve and the Intelligence Community corpus. No prior system has been evaluated on both corpora. Our model outperforms previous systems on both corpora, achieving 0.74 BLANC F1 on the Intelligence Community corpus and 0.70 F1 on the HiEve corpus, respectively a 15 and 5 percentage point improvement over previous models.</abstract>
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%0 Conference Proceedings
%T Detecting Subevents using Discourse and Narrative Features
%A Aldawsari, Mohammed
%A Finlayson, Mark
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F aldawsari-finlayson-2019-detecting
%X Recognizing the internal structure of events is a challenging language processing task of great importance for text understanding. We present a supervised model for automatically identifying when one event is a subevent of another. Building on prior work, we introduce several novel features, in particular discourse and narrative features, that significantly improve upon prior state-of-the-art performance. Error analysis further demonstrates the utility of these features. We evaluate our model on the only two annotated corpora with event hierarchies: HiEve and the Intelligence Community corpus. No prior system has been evaluated on both corpora. Our model outperforms previous systems on both corpora, achieving 0.74 BLANC F1 on the Intelligence Community corpus and 0.70 F1 on the HiEve corpus, respectively a 15 and 5 percentage point improvement over previous models.
%R 10.18653/v1/P19-1471
%U https://aclanthology.org/P19-1471
%U https://doi.org/10.18653/v1/P19-1471
%P 4780-4790

Markdown (Informal)

[Detecting Subevents using Discourse and Narrative Features](https://aclanthology.org/P19-1471) (Aldawsari & Finlayson, ACL 2019)

• Detecting Subevents using Discourse and Narrative Features (Aldawsari & Finlayson, ACL 2019)

ACL

• Mohammed Aldawsari and Mark Finlayson. 2019. Detecting Subevents using Discourse and Narrative Features. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4780–4790, Florence, Italy. Association for Computational Linguistics.