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Treasures Outside Contexts: Improving Event Detection via Global Statistics ...
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Abstract:
Anthology paper link: https://aclanthology.org/2021.emnlp-main.206/ Abstract: Event detection (ED) aims at identifying event instances of specified types in given texts, which has been formalized as a sequence labeling task. As far as we know, existing neural-based ED models make decisions relying on the contextual semantic features of each word in the input text, which we find is easy to get confused by varied contexts in the test stage. To this end, we come up with the idea of introducing a set of statistical features from word-event co-occurrence frequencies in the entire training set to cooperate with the contextual features. Specifically, we propose a Semantic and Statistic-Joint Discriminative Network (SS-JDN) consisting of a semantic feature extractor, a statistical feature extractor, and a joint event discriminator. In experiments, SS-JDN effectively exceeds ten recent state-ofthe-art (SOTA) baseline methods on ACE2005 and KBP2015 benchmark datasets. Further, we perform extensive experiments to ...
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Keyword:
Computational Linguistics; Information Extraction; Machine Learning; Machine Learning and Data Mining; Natural Language Processing
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URL: https://underline.io/lecture/37476-treasures-outside-contexts-improving-event-detection-via-global-statistics
https://dx.doi.org/10.48448/jttq-mq83
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Exploring language aptitude: Views from psychology, the language sciences, and cognitive neuroscience. Susanne M. Reiterer (ed.) (2018)
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In: Porta Linguarum: revista internacional de didáctica de las lenguas extranjeras, ISSN 1697-7467, Nº. 36, 2021, pags. 281-282 (2021)
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Metacognition in Language Learning and Teaching. Åsta Haukås, Camilla Bjørke, and Magne Dypedahl (eds.) (2018). Abingdon: Routledge, ISBN: 978-11-3863-338-4
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In: Porta Linguarum: revista internacional de didáctica de las lenguas extranjeras, ISSN 1697-7467, Nº. 31, 2019, pags. 149-150 (2019)
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Chinese dental students' knowledge and attitudes toward HIV/AIDS
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| 10 |
The Neuronal Correlates of Digits Backward Are Revealed by Voxel-Based Morphometry and Resting-State Functional Connectivity Analyses
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