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19805710_S11
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19232615_S7
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24101715_S4
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25572620_S4
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24352377_S8
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24352377_S10
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22079469_S4
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22079469_S5
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23101741_S2
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24229362_S5
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24229362_S6
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25087908_S5
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24930383_S6
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18523583_S9
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15713151_S7
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21599896_S11
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21892106_S7
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21892106_S8
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19433686_S5
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24213956_S6
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15482502_S10
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25022743_S5
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25022743_S6
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26746121_S3
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26746121_S4
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19814586_S6
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19814586_S7
[ "secondary", "efficacy", "endpoints", "included", "pain", "intensity", "difference", "(", "pid", ")", "and", "pain", "relief", "(", "pr", ")", "throughout", "the", "60-min", "post-dose", "assessment", "period", "." ]
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22151475_S9
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22151475_S10
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22151475_S11
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23578015_S3
[ "primary", "endpoint", "was", "the", "adhd", "rating", "scale", "(", "adhd-rs", ")", "." ]
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Dataset Card for Dataset Name

Corpus for token classification of primary and secondary outcomes in scientific articles sentences, in BIO format.

Dataset Details

Dataset Description

Filtered the EBM-NLP corpus Outcomes subset and did the following processing:

  • split examples into sentences and get the entities for each sentence
  • verify in each sentences mentions of primary and secondary outcomes using regular expression (with synonyms)
  • tagged all outcomes according to type when the regex was found in a sentence
  • removed entity tags for all sentences that were not detected as containing primary or secondary outcome
  • kept the examples at sentence level
  • kept original train/test set

Then added data from A. koroleva on primary outcomes (manually annotated corpus). Part of this data is

Then added manually annotated data from a study on primary outcome switching in colorectal cancer articles (this data is only used in test set).

Finally as the filtered EBM-NLP contained a lot of sequences without entities in the end, we sampled from these sequences so that we have an equal number of sequences with and without entities, in train and test set.

  • Curated by: Mathieu Laï-king
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  • Language(s) (NLP): English
  • License: None

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From 2 existing corpus one from A. Koroleva and the other is EBM-NLP.

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