freelance javascripter
nlp('John eats glue!').match('john').text()
nlp('John eats glue').match('john eats #Noun').text()
nlp('John eats glue').match('john . glue').text()
nlp('John always ravenously eats his glue').match('john * eats').text()
nlp('he is really, really good').match('#Adverb+ good').terms().text()
nlp('John eats glue').match('john always? eats glue').text()
nlp('John eats glue').match('john (eats|sniffs|wears) .').text()
nlp('it is raining and had rained').match('#Verb /rain[ing|ed]/').text()
nlp('five maids a-milking.').match('[<num>#Value] maids a milking').text()
nlp('i saw ralf eat the glue, Mrs Hoover.').match('[<act>#Verb the #Noun]').groups()
{
let doc = nlp(`If you can't find metal stucco lath... Use carbon-fiber stucco lath!`)
let m = doc.match('#Verb [.{1,2}] stucco lath')
return m.groups(0).out('array')
}
{
let doc = nlp('that leaves 5 in the bank, and 80 hundred we need')
let m = doc.match('[<have>#Value+] in the bank and [<need>#Value+] we need')
return m.groups()
}
md`
if you want to quickly return the group, and ignore the rest, throw the name in the 2nd param:
`
nlp('turn left and slay the dragon').match('turn [.]',0).text()
nlp('John eats glue').match('^john #Verb').out('text')
nlp('John eats glue').match('eats glue$').terms().out('array')
nlp('Homer Simpson and Homer Adkins').match('homer !simpson').text()
nlp('one two three').match('one !foo? two').text()
nlp('this is tricky.').match('this is !not? #Adjective').text()
nlp('homer j j j j simpson').match('homer #Acronym{2,6} simpson').text()
nlp(`funny story`).match('/nny$/').out() //suffix lookup
nlp('Why all the pearls? Why all the hair?').match('/ear/').text() //'infix' lookup
nlp(`Homer J. Simpson`).match('#Person').match('#Acronym').text()
nlp(`Homer J. Simpson`).match('(#Person && #Acronym)').text()
nlp(`we're not gonna take it. No! We're not gonna take it.`).match('@hasExclamation').text()
nlp(`we're not gonna take it. No!`).match('(@hasContraction|@isTitleCase)+').out('array')
{
let doc = nlp("I'm looking for Amanda Hugginkiss")
// cache the root form of each word
doc.compute('root')
// use a 'soft' lookup:
let m = doc.match('{look} for')
// print our result:
return m.text()
}
nlp('Albuquerque Holiday Inn').match('~albequerque~').text()
{
let doc = nlp('my son is also named bort')
return doc.match('~bart~', null, {fuzzy:0.7}).text()
}
{
let json=[
{choices:[{word:'bort'}, {tag:'MaleName'}]},
{word:'license'},
{word:'plates', optional:true}
]
return nlp(`Repeat, we are sold out of Bort license plates.`).match(json).text()
}
nlp.parseMatch('we (walked|talked) in? #Month')
nlp.version
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