Abstract: Span-based joint extraction models have achieved excellent results in named entity recognition and relation extraction. These models regard text spans as candidate entities and span tuples as candidate relation tuples. span semantic representations are shared in both entity recognition and relation extraction, while existing models cannot well capture semantics of these candidate entities and relations. To address these problems, a span-based joint extraction framework with attention-based semantic re- presentations is proposed. Specially, attentions are utilized to calculate semantic representations, includ- ing span-specific and contextual ones. Experiments show that our model outperforms previous systems and achieves state-of-the-art results on ACE2005, CoNLL2004 and ADE.

Key words: span, entity recognition, relation extraction, joint extraction