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Anaphora resolution is an important task for information extraction across a range of languages, text genres, and domains, motivating the need for methods that do not require large annotated datasets.
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Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2019 · 1907
Earlier work this paper cites.
Adaptive mixtures of local experts
Robert A Jacobs, Michael I Jordan, Steven J Nowlan, and Geoffrey E Hinton. 1991 · 1991
Earlier work this paper cites.
Accommodating context change
Bonnie Webber and Breck Baldwin. 1992 · 1992
Earlier work this paper cites.
Hierarchical mixtures of experts and the em algorithm
Michael I Jordan and Robert A Jacobs. 1994 · 1994
Earlier work this paper cites.
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Doug Downey, Oren Etzioni, and Stephen Soderland. 2005 · 2005
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Inferring experimental procedures from text-based representations of chemical reactions
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Later among the works it cites.
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Large language models are zero-shot clinical information extractors
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Free the plural: Unrestricted split-antecedent anaphora resolution
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ChEMU-ref: A corpus for modeling anaphora resolution in the chemical domain
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