brainsteam.co.uk/brainsteam/content/replies/2022/11/23/1669234351.md

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2022-11-23T20:12:31
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2022-11-23T20:12:31.341810+00:00
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2210.07188.pdf
false __world__ false KRvuAmtrEe26TOOrc3o_zA
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https://hypothes.is/a/KRvuAmtrEe26TOOrc3o_zA https://hyp.is/KRvuAmtrEe26TOOrc3o_zA/arxiv.org/pdf/2210.07188.pdf https://hypothes.is/api/annotations/KRvuAmtrEe26TOOrc3o_zA
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acct:ravenscroftj@hypothes.is
acct:ravenscroftj@hypothes.is
group:__world__
acct:ravenscroftj@hypothes.is
data-annotation
coreference
NLProc
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26459 26292 TextPositionSelector
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an algorithm with high precision on LitBank orOntoNotes would miss a huge percentage of rele-vant mentions and entities on other datasets (con-straining our analysis) re mentions of differentlengths. and when annotating newtexts and TextQuoteSelector
https://arxiv.org/pdf/2210.07188.pdf
these datasets have the most limited/constrained definitions for co-reference and what should be marked up so it makes sense that precision is poor in these datasets 2022-11-23T20:12:31.341810+00:00 https://arxiv.org/pdf/2210.07188.pdf acct:ravenscroftj@hypothes.is
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James Ravenscroft
https://arxiv.org/pdf/2210.07188.pdf
data-annotation
coreference
NLProc
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reply /replies/2022/11/23/1669234351
an algorithm with high precision on LitBank orOntoNotes would miss a huge percentage of rele-vant mentions and entities on other datasets (con-straining our analysis)
these datasets have the most limited/constrained definitions for co-reference and what should be marked up so it makes sense that precision is poor in these datasets