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

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---
date: '2022-11-23T20:12:31'
hypothesis-meta:
created: '2022-11-23T20:12:31.341810+00:00'
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title:
- 2210.07188.pdf
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- data-annotation
- coreference
- NLProc
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type: TextPositionSelector
- exact: '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) '
prefix: re mentions of differentlengths.
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source: https://arxiv.org/pdf/2210.07188.pdf
text: 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
updated: '2022-11-23T20:12:31.341810+00:00'
uri: https://arxiv.org/pdf/2210.07188.pdf
user: acct:ravenscroftj@hypothes.is
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display_name: James Ravenscroft
in-reply-to: https://arxiv.org/pdf/2210.07188.pdf
tags:
- data-annotation
- coreference
- NLProc
- hypothesis
type: annotation
url: /annotation/2022/11/23/1669234351
---
<blockquote>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) </blockquote>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