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- W4297029908 abstract "Entity linking (EL) in written language domains has been extensively studied, but EL of spoken language is still unexplored. We propose a conceptually simple and highly effective two-stage approach to tackle this issue. The first stage retrieves candidates with a dual encoder, which independently encodes mention context and entity descriptions. Each candidate is then reranked by a LUKE-based cross-encoder, which concatenates the mention and entity description. Different from previous cross-encoder which takes only words as input, our model adds entities into input. Experiments demonstrate that our model does not need large-scale training on Wikipedia corpus, and outperforms all previous models with or without Wikipedia training. Our approach ranks the $$1^textrm{st}$$ in NLPCC 2022 Shared Task on Speech EL Track 2 (Entity Disambiguation-Only)." @default.
- W4297029908 created "2022-09-25" @default.
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- W4297029908 date "2022-01-01" @default.
- W4297029908 modified "2023-10-13" @default.
- W4297029908 title "Enhancing Entity Linking with Contextualized Entity Embeddings" @default.
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- W4297029908 doi "https://doi.org/10.1007/978-3-031-17189-5_19" @default.
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