Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics 2019
DOI: 10.18653/v1/p19-1471
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Detecting Subevents using Discourse and Narrative Features

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 a… Show more

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Cited by 29 publications
(23 citation statements)
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“…To cope with this task, both Araki et al (2014) and Glavaš andŠnajder (2014) introduced a variety of features and employed logistic regression models for classifying event pairs into subevent relations (PARENT-CHILD and CHILD-PARENT, coreference (COREF), and no relation (NOREL). Aldawsari and Finlayson (2019) further extended the characterization with more features on the discourse and narrative aspects. Zhou et al (2020a) presented a data-driven method by fine-tuning a time duration-aware BERT (Devlin et al, 2019) on corpora of time mentions, and used the estimation of time duration to predict subevent relations.…”
Section: Related Workmentioning
confidence: 99%
“…To cope with this task, both Araki et al (2014) and Glavaš andŠnajder (2014) introduced a variety of features and employed logistic regression models for classifying event pairs into subevent relations (PARENT-CHILD and CHILD-PARENT, coreference (COREF), and no relation (NOREL). Aldawsari and Finlayson (2019) further extended the characterization with more features on the discourse and narrative aspects. Zhou et al (2020a) presented a data-driven method by fine-tuning a time duration-aware BERT (Devlin et al, 2019) on corpora of time mentions, and used the estimation of time duration to predict subevent relations.…”
Section: Related Workmentioning
confidence: 99%
“…trained a logistic regression classifier using a range of lexical and syntactic features and then used Integer Linear Programming (ILP) to enforce document-level coherence for constructing coherent event hierarchies from news. Recently, (Aldawsari and Finlayson, 2019) outperformed previous models for subevent relation prediction using a linear SVM classifier, by introducing several new discourse features and narrative features.…”
Section: Related Workmentioning
confidence: 99%
“…RED has 530 intra-sentence and 415 cross-sentence subevent relations.12 HiEve annotated 3,200 event mentions and their subevents as well as coreference relations in 100 documents. We first extended the subevent annotations using transitive closure rules and coreference relationsAldawsari and Finlayson, 2019), which produces 490 intrasentence and 3.1K cross-sentence subevent relations.…”
mentioning
confidence: 99%
“…We also captured the semantic frame of the event using SEMAFOR (Das et al, 2010), encoded as one-hot vector. Thompson, 1988) is useful for many NLP tasks including sentiment analysis (Bhatia et al, 2015), information extraction (Maslennikov and Chua, 2007), and subevent detection (Aldawsari and Finlayson, 2019). We used Feng-Hirst discourse parser (Feng and Hirst, 2014) to build a discourse tree of each text, and post-processed the output to build a graph (Neumann, 2015).…”
Section: Modelmentioning
confidence: 99%