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- W2905462022 abstract "Joint entity and relation extraction is to detect entity and relation using a single model. In this paper, we present a novel unified joint extraction model which directly tags entity and relation labels according to a query word position p, i.e., detecting entity at p, and identifying entities at other positions that have relationship with the former. To this end, we first design a tagging scheme to generate n tag sequences for an n-word sentence. Then a position-attention mechanism is introduced to produce different sentence representations for every query position to model these n tag sequences. In this way, our method can simultaneously extract all entities and their type, as well as all overlapping relations. Experiment results show that our framework performances significantly better on extracting overlapping relations as well as detecting long-range relation, and thus we achieve state-of-the-art performance on two public datasets." @default.
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- W2905462022 date "2019-07-17" @default.
- W2905462022 modified "2023-10-01" @default.
- W2905462022 title "Joint Extraction of Entities and Overlapping Relations Using Position-Attentive Sequence Labeling" @default.
- W2905462022 doi "https://doi.org/10.1609/aaai.v33i01.33016300" @default.
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