In this paper, we focus on improving Event Extraction (EE) by
\nincorporating visual knowledge with words and phrases from text
\ndocuments. We \u0080rst discover visual pa\u008aerns from large-scale textimage
\npairs in a weakly-supervised manner and then propose a
\nmultimodal event extraction algorithm where the event extractor is
\njointly trained with textual features and visual pa\u008aerns. Extensive
\nexperimental results on benchmark data sets demonstrate that the
\nproposed multimodal EE method can achieve signi\u0080cantly be\u008aer
\nperformance on event extraction: absolute 7.1% F-score gain on
\nevent trigger labeling and 8.5% F-score gain on event argument
\nlabeling.<\/p>\n","protected":false},"excerpt":{"rendered":"
In this paper, we focus on improving Event Extraction (EE) by incorporating visual knowledge with words and phrases from text documents. We \u0080rst discover visual pa\u008aerns from large-scale textimage pairs in a weakly-supervised manner and then propose a multimodal event extraction algorithm where the event extractor is jointly trained with textual features and visual pa\u008aerns. 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