wehub-resource-sync
37 lines
1.5 KiB
JavaScript
37 lines
1.5 KiB
JavaScript
import test from 'tape'
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import nlp from '../_lib.js'
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const here = '[two/multiword] '
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test('multiword OR', function (t) {
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let m = nlp('i saw the canadian senate floor').match('the (united states|canadian) senate')
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t.equal(m.text(), 'the canadian senate', here + 'one-word multi')
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m = nlp('i saw the united states senate floor').match('the (united states|canadian) senate')
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t.equal(m.text(), 'the united states senate', here + 'two-word multi')
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m = nlp('i saw the united states of america senate floor').match(
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'the (brazillian|united states of america|canadian) senate'
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)
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t.equal(m.text(), 'the united states of america senate', here + 'four-word multi')
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m = nlp('i saw him').match('i saw (the person|#Pronoun)')
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t.equal(m.text(), 'i saw him', here + 'single-tag multi')
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m = nlp('i saw everything').match('i saw (the person|#Pronoun)?')
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t.equal(m.text(), 'i saw', here + 'multi-word optional')
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m = nlp('i saw the person go').match('i saw [(the hat|#Pronoun)]')
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t.equal(m.text(), '', here + 'multi-word negative')
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m = nlp('i saw the person go').match('i saw [(the person|#Pronoun)]', 0)
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t.equal(m.text(), 'the person', here + 'multi-word group')
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m = nlp('i saw tina turner go').match('i saw [<match>(the person|#Pronoun|tina turner)]', 'match')
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t.equal(m.text(), 'tina turner', here + 'multi-word named-group')
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m = nlp('i saw tina harris go').match('i saw [<match>(the person|#Pronoun|tina turner)]', 'match')
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t.equal(m.text(), '', here + 'multi-word named-group negative')
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t.end()
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})
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