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dc.contributor.authorXu, Guangxuan
dc.contributor.authorToro Isaza, Paulina
dc.contributor.authorLi, Moshi
dc.contributor.authorOloko, Akintoye
dc.contributor.authorYao, Bingsheng
dc.contributor.authorSanctos, Cassia
dc.contributor.authorAdebiyi, Aminat
dc.contributor.authorHou, Yufang
dc.contributor.authorPeng, Nanyun
dc.contributor.authorWang, Dakuo
dc.identifier.citationGuangxuan Xu, Paulina Toro Isaza, Moshi Li, Akintoye Oloko, Bingsheng Yao, Cassia Sanctos, Aminat Adebiyi, Yufang Hou, Nanyun Peng, & Dakuo Wang. (2023). NECE: Narrative Event Chain Extraction Toolkit.en_US
dc.description.abstractTo understand a narrative, it is essential to comprehend its main characters and the associated major events; however, this can be challenging with lengthy and unstructured narrative texts. To address this, we introduce NECE, an open-access, document-level toolkit that automatically extracts and aligns narrative events in the temporal order of their occurrence using sliding window method. Through extensive human evaluations, we have confirmed the high quality of the NECE toolkit, and external validation has demonstrated its potential for application in downstream tasks such as question answering and bias analysis. The NECE toolkit includes both a Python library and a user-friendly web interface; the latter offers custom visualizations of event chains and easy navigation between graphics and text to improve reading efficiency and experience.en_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.titleNECE: Narrative Event Chain Extraction Toolkiten_US

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Attribution-NonCommercial-NoDerivs 3.0 United States
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States