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. Extensive experimental results on benchmark data sets demonstrate that the proposed multimodal EE method can achieve signi\u0080cantly be\u008aer performance on event extraction: absolute 7.1% F-score gain on event trigger labeling and 8.5% F-score gain on event argument labeling.<\/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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