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A Sequence Learning Method for Domain-Specific Entity Linking
2018
Proceedings of the Seventh Named Entities Workshop
Recent collective Entity Linking studies usually promote global coherence of all the mapped entities in the same document by using semantic embeddings and graphbased approaches. Although graph-based approaches are shown to achieve remarkable results, they are computationally expensive for general datasets. Also, semantic embeddings only indicate relatedness between entity pairs without considering sequences. In this paper, we address these problems by introducing a two-fold neural model. First,
doi:10.18653/v1/w18-2403
dblp:conf/aclnews/InanD18
fatcat:jec5nd4nnrcmhbzn33oob7lk64