freelance javascripter
nlp('Hi Bart. Hello mrs Cumberdale.').document
{
let doc = nlp("that's it. Back to Winnipeg!")
let json = doc.document //grab it
json[1][0].text = json[1][0].text.toUpperCase() //mess around
return doc.text()
}
// See the pointer as just array indexes
// [1st sentence, start on 2nd term, end 3rd term, expected term-id]
nlp("that's it. Back to Winnipeg!").match('#Place').pointer[0]
{
let doc = nlp('before middle after')
let m = doc.match('middle')
// add an extra word
doc.match('before').insertAfter('EXTRA')
return m.text() // match is consistent
}
{
let doc = nlp(`he hadn't really walked away.`)
let tmp = doc.clone()
// throw-around our temporary text
tmp.contractions().expand()
tmp.remove('(#Adverb|not)')
// run the analysis on a normalized form
if(tmp.has('had #PastTense')){
return `[found-verb-tense] - ${doc.text()}`
}
}
{
let doc = nlp('January the 12th of 2022 at 3pm')
// mutate a clone
let tmp = doc.clone()
tmp.remove('(the|of|at|#Time)')
tmp.numbers().toCardinal()
// now 'january 12 2022'
let tmpYear = tmp.match('#Month . [#Value]$', 0)
// '2022'
// get the match in the original document
let year = doc.match(tmpYear) // just throw it in..
// '2022' (a different one)
// prove we're in the original
return year.grow('*').text()
}
{
let doc = nlp('one two three')
let tmp = doc.clone()
// mutate the original
doc.remove('two')
// return a partial match
return doc.match(tmp).text()
}
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