2012 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support 2012
DOI: 10.1109/cogsima.2012.6188406
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Improving event co-reference by context extraction and dynamic feature weighting

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Cited by 13 publications
(8 citation statements)
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“…The existing literature on supervised event coreference resolution primarily focuses on designing pairwise classifier based on the surface linguistic features such as lexical features comprising of lemma and part-of-speech tag similarity of event words (Bejan and Harabagiu, 2010;Lee et al, 2012;Liu et al, 2014;Yang et al, 2015;Cremisini and Finlayson, 2020), argument overlap McConky et al, 2012;Sangeetha and Arock, 2012;Bejan and Harabagiu, 2014;Yang et al, 2015;Choubey and Huang, 2017), semantic similarity based on lexical resources such as wordnet (Bejan and Harabagiu, 2010;Liu et al, 2014;Yu et al, 2016) and word embeddings (Yang et al, 2015;Choubey and Huang, 2017;Kenyon-Dean et al, 2018;Barhom et al, 2019;Zuo et al, 2019;Pandian et al, 2020;Sahlani et al, 2020;, and discourse features such as token and sentence distance (Liu et al, 2014;Cybulska and Vossen, 2015). The resulting classifier is used to cluster event mentions.…”
Section: Related Workmentioning
confidence: 99%
“…The existing literature on supervised event coreference resolution primarily focuses on designing pairwise classifier based on the surface linguistic features such as lexical features comprising of lemma and part-of-speech tag similarity of event words (Bejan and Harabagiu, 2010;Lee et al, 2012;Liu et al, 2014;Yang et al, 2015;Cremisini and Finlayson, 2020), argument overlap McConky et al, 2012;Sangeetha and Arock, 2012;Bejan and Harabagiu, 2014;Yang et al, 2015;Choubey and Huang, 2017), semantic similarity based on lexical resources such as wordnet (Bejan and Harabagiu, 2010;Liu et al, 2014;Yu et al, 2016) and word embeddings (Yang et al, 2015;Choubey and Huang, 2017;Kenyon-Dean et al, 2018;Barhom et al, 2019;Zuo et al, 2019;Pandian et al, 2020;Sahlani et al, 2020;, and discourse features such as token and sentence distance (Liu et al, 2014;Cybulska and Vossen, 2015). The resulting classifier is used to cluster event mentions.…”
Section: Related Workmentioning
confidence: 99%
“…Existing event coreference resolvers have been evaluated on different corpora, such as MUC (e.g., Humphreys et al (1997)), ACE (e.g., Ahn (2006), Chen and Ji (2009), McConky et al (2012), Sangeetha and Arock (2012), Ng (2015, 2016), Krause et al (2016)), OntoNotes (e.g., Chen et al (2011)), the Intelligence Community corpus (e.g., Cybulska and Vossen (2012), , ), the ECB corpus (e.g., Lee et al (2012), Bejan andHarabagiu (2014)) and its extension ECB+ (e.g., Yang et al (2015)), and ProcessBank (e.g., Araki and Mitamura (2015)). The newest event coreference corpora are the ones used in the KBP 2015 and 2016 Event Nugget Detection and Coreference shared tasks, in which the best performers in 2015 and 2016 are RPI's system (Hong et al, 2015) and UTD's system , respectively.…”
Section: Related Workmentioning
confidence: 99%
“…Improvements to this standard approach include the use of feature weighting to train a better model (McConky et al, 2012), and graph-based clustering algorithms to produce event coreference clusters (Chen and Ji, 2009;Sangeetha and Arock, 2012). Chen et al (2011) train multiple classifiers to handle coreference between event mentions of different syntactic types (e.g., verb-noun coreference, noun-noun coreference) on the OntoNotes corpus (Pradhan et al, 2007).…”
Section: Related Workmentioning
confidence: 99%