Datasets:
idiom stringlengths 6 47 | is_pie bool 2
classes | tokens sequence | ner_tags sequence |
|---|---|---|---|
get to grips with | true | [
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call it a day | true | [
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run a mile | true | [
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on the button | true | [
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separate the sheep from the goats | true | [
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under the sun | true | [
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throw a wobbly | true | [
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slip through the net | true | [
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come to a head | true | [
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hold someone's hand | false | [
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break even | true | [
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go it alone | false | [
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save someone's skin | true | [
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slip through the net | true | [
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sit on the fence | true | [
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out of order | true | [
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get the picture | true | [
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make the cut | false | [
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claim to fame | true | [
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in this day and age | true | [
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catch fire | true | [
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bear in mind | true | [
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the rest is history | true | [
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lay a finger on | true | [
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hold the line | true | [
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toe the line | true | [
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take root | true | [
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out of line | true | [
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all and sundry | true | [
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in the can | true | [
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under a cloud | true | [
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take the mickey | true | [
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be my guest | true | [
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come to a head | true | [
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slippery slope | true | [
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all over bar the shouting | true | [
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read between the lines | true | [
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be my guest | false | [
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make history | true | [
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blow someone's cover | true | [
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on top of the world | true | [
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call the tune | false | [
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Dataset Card for PIEs corpus
Dataset Summary
This corpus is a collection of 57170 potentially idiomatic expressions (PIEs) based on the British National Corpus, prepaired for NER task. Each of the objects is comes with a contextual set of tokens, BIO tags and boolean label. The data sources are:
Detailed data preparation pipeline can be found here
Supported Tasks and Leaderboards
Token classification (NER)
Languages
English
Dataset Structure
Data Instances
For each instance there is a string with target idiom, tokenized by word text with context of idiom usage, corresponded BIO tags
and boolean label is_pie. This tag determines whether or not a collocation is considered an idiom in a given context.
For a PIE dataset the choice was determined by the original PIE_label. For MAGPIE a threshold of 0.75 confidence coefficient was chosen.
An example from the train set looks like the following:
{'idiom': "go public"
'is_pie': True
'tokens': [ "Private", "dealers", "in", "the", "States", "go", "public" ]
'ner_tags': [ 0, 0, 0, 0, 0, 1, 2 ]
}
Where NER tags is {0: 'O', 1: 'B-PIE', 2: 'I-PIE'}
Data Fields
- idiom: a string containg original PIE
- is_pie: a boolean label determining whether a PIE can be considered an idiom in a given context
- tokens: sequence of word tkenized string with PIE usage context
- ner_tags: corresponded BIO tags for word tokens
Data Splits
The SNLI dataset has 3 splits: train, validation, and test.
| Dataset Split | Number of Instances in Split |
|---|---|
| Train | 45,736 |
| Validation | 5,717 |
| Test | 5,717 |
Dataset Creation
Source Data
Initial Data Collection and Normalization
Additional Information
Licensing Information
Corpus and it's sources are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Citation Information
PIE Corpus (Haagsma, H. (Creator), Bos, J. (Contributor), Plank, B. (Contributor), University of Groningen.)
MAGPIE: A Large Corpus of Potentially Idiomatic Expressions (Haagsma et al., LREC 2020)
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