brainsteam.co.uk/brainsteam/content/annotations/2022/11/23/1669234701.md

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---
date: '2022-11-23T20:18:21'
hypothesis-meta:
created: '2022-11-23T20:18:21.503899+00:00'
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title:
- 2210.07188.pdf
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tags:
- data-annotation
- coreference
- NLProc
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- exact: 'Our annotators achieve thehighest precision with OntoNotes, suggesting
thatmost of the entities identified by crowdworkers arecorrect for this dataset. '
prefix: 'ntoNotes, GUM, Lit-Bank, ARRAU: '
suffix: In terms of F1 scores, thedatase
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source: https://arxiv.org/pdf/2210.07188.pdf
text: interesting that the mention detection algorithm gives poor precision on OntoNotes
and the annotators get high precision. Does this imply that there are a lot of
invalid mentions in this data and the guidelines for ontonotes are correct to
ignore generic pronouns without pronominals?
updated: '2022-11-23T20:18:21.503899+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/2022/11/23/1669234701
---
<blockquote>Our annotators achieve thehighest precision with OntoNotes, suggesting thatmost of the entities identified by crowdworkers arecorrect for this dataset. </blockquote>interesting that the mention detection algorithm gives poor precision on OntoNotes and the annotators get high precision. Does this imply that there are a lot of invalid mentions in this data and the guidelines for ontonotes are correct to ignore generic pronouns without pronominals?