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compromise - npm Package Compare versions

Comparing version 7.0.16 to 7.0.17

126

docs/generic.js

@@ -0,1 +1,3 @@

'use strict';
//methods on the generic Result prototype

@@ -6,5 +8,4 @@ module.exports = {

desc: 'return a handy array of meta-data for this subset. Default subset is sentences, but it can be anything.',
example: `nlp('The stage was set for the Alan Parsons Project! Which I believe was some sort of hovercraft.').data()
//[{normal:'the stage was set...'}]`,
returns: 'array',
example: 'nlp(\'The stage was set for the Alan Parsons Project! Which I believe was some sort of hovercraft.\').data()\n//[{normal:\'the stage was set...\'}]',
returns: 'array'
},

@@ -14,4 +15,3 @@ found: {

returns: 'bool',
example: `nlp('oh say can you see?').match('see').found
//true`
example: 'nlp(\'oh say can you see?\').match(\'see\').found\n//true'
},

@@ -21,4 +21,3 @@ all: {

returns: 'Text',
example: `nlp('this is yelling').verbs().toTitleCase().all().out()
//this IS yelling`
example: 'nlp(\'this is yelling\').verbs().toTitleCase().all().out()\n//this IS yelling'
},

@@ -28,3 +27,3 @@ debug: {

returns: 'Text',
example: `nlp('wayne’s world, party time, excellent- weeeyooweeeyoo!').debug().out()`
example: 'nlp(\'wayne\u2019s world, party time, excellent- weeeyooweeeyoo!\').debug().out()'
},

@@ -34,4 +33,3 @@ out: {

returns: 'Text',
example: `nlp('you might say there’s a little Uter in all of us').match('#Adjective uter').out('html')
//<span><span class="nl-Adjective">little</span>&nbsp;<span class="nl-Person nl-FirstName">Uter</span></span>`
example: 'nlp(\'you might say there\u2019s a little Uter in all of us\').match(\'#Adjective uter\').out(\'html\')\n//<span><span class="nl-Adjective">little</span>&nbsp;<span class="nl-Person nl-FirstName">Uter</span></span>'
},

@@ -41,4 +39,3 @@ whitespace: {

returns: 'Text',
example: `nlp('We like Roy! We like Roy!').whitespace.before(' ').out()
//We like Roy! We like Roy!`
example: 'nlp(\'We like Roy! We like Roy!\').whitespace.before(\' \').out()\n//We like Roy! We like Roy!'
},

@@ -48,5 +45,4 @@ normalize: {

returns: 'Text',
example: `nlp(' so... you like DONUTS? have all the donuts in the WORLD!!!').normalize().sentences(0).out()
//So you like donuts?`
},
example: 'nlp(\' so... you like DONUTS? have all the donuts in the WORLD!!!\').normalize().sentences(0).out()\n//So you like donuts?'
}
},

@@ -57,4 +53,3 @@ array: {

returns: 'Text',
example: `nlp('would somebody please think of the children').clone().toUpperCase().parent.out()
//would somebody please think of the children`
example: 'nlp(\'would somebody please think of the children\').clone().toUpperCase().parent.out()\n//would somebody please think of the children'
},

@@ -64,4 +59,3 @@ length: {

returns: 'int',
example: `nlp('jackie kennedy and aristotle onassis').people().length
//2`
example: 'nlp(\'jackie kennedy and aristotle onassis\').people().length\n//2'
},

@@ -71,4 +65,3 @@ slice: {

returns: 'Text',
example: `nlp('Homer, have you been eating that sandwich again?').terms().slice(0, 3).out()
//Homer, have you`
example: 'nlp(\'Homer, have you been eating that sandwich again?\').terms().slice(0, 3).out()\n//Homer, have you'
},

@@ -78,4 +71,3 @@ sort: {

returns: 'Text',
example: `nlp('Larry, Curly, and Moe').people().sort('alphabetical').out('array')
//Curly, Larry, Moe`
example: 'nlp(\'Larry, Curly, and Moe\').people().sort(\'alphabetical\').out(\'array\')\n//Curly, Larry, Moe'
},

@@ -85,4 +77,3 @@ concat: {

returns: 'Text',
example: `nlp('My name is Otto.').concat(nlp('i love to get blotto')).length()
//2`
example: 'nlp(\'My name is Otto.\').concat(nlp(\'i love to get blotto\')).length()\n//2'
},

@@ -92,5 +83,4 @@ flatten: {

returns: 'Text',
example: `nlp('sex cauldron? I thought they closed that place down.').flatten().length()
//1`
},
example: 'nlp(\'sex cauldron? I thought they closed that place down.\').flatten().length()\n//1'
}
},

@@ -102,4 +92,3 @@

returns: 'Text',
example: `nlp('jupiter, pluto and mars').nouns().toTitleCase().all().out()
//Jupiter, Pluto and Mars.`
example: 'nlp(\'jupiter, pluto and mars\').nouns().toTitleCase().all().out()\n//Jupiter, Pluto and Mars.'
},

@@ -109,4 +98,3 @@ toUpperCase: {

returns: 'Text',
example: `nlp('Dental plan. Lisa needs braces.').match('dental .').toUpperCase().out()
//DENTAL PLAN.`
example: 'nlp(\'Dental plan. Lisa needs braces.\').match(\'dental .\').toUpperCase().out()\n//DENTAL PLAN.'
},

@@ -116,4 +104,3 @@ toLowerCase: {

returns: 'Text',
example: `nlp('Careful! They’re RUFFLED!!').toLowerCase().out()
//careful! they’re ruffled!!`
example: 'nlp(\'Careful! They\u2019re RUFFLED!!\').toLowerCase().out()\n//careful! they\u2019re ruffled!!'
},

@@ -123,5 +110,4 @@ toCamelCase: {

returns: 'Text',
example: `nlp('natural language processing').toCamelCase().out()
//NaturalLanguageProcessing`
},
example: 'nlp(\'natural language processing\').toCamelCase().out()\n//NaturalLanguageProcessing'
}
},

@@ -132,4 +118,3 @@ punctuation: {

returns: 'Text',
example: `nlp('natural language processing').hyphenate().out()
//natural-language-processing`
example: 'nlp(\'natural language processing\').hyphenate().out()\n//natural-language-processing'
},

@@ -139,5 +124,4 @@ dehyphenate: {

returns: 'Text',
example: `nlp('natural-language processing').dehyphenate().out()
//natural language processing`
},
example: 'nlp(\'natural-language processing\').dehyphenate().out()\n//natural language processing'
}

@@ -150,4 +134,3 @@ },

returns: 'Text',
example: `nlp('stupid flanders').match('flanders').insertBefore('sexy').all().out()
//stupid sexy flanders`
example: 'nlp(\'stupid flanders\').match(\'flanders\').insertBefore(\'sexy\').all().out()\n//stupid sexy flanders'
},

@@ -157,4 +140,3 @@ insertAfter: {

returns: 'Text',
example: `nlp('i know so many words').insertAfter('bigly').all().out()
//i know so many words bigly`
example: 'nlp(\'i know so many words\').insertAfter(\'bigly\').all().out()\n//i know so many words bigly'
},

@@ -164,5 +146,4 @@ insertAt: {

returns: 'Text',
example: `nlp('so you are from Africa?').insertAt(2, 'like,').all().out()
//so you are like, from africa?`
},
example: 'nlp(\'so you are from Africa?\').insertAt(2, \'like,\').all().out()\n//so you are like, from africa?'
}
},

@@ -174,4 +155,3 @@

returns: 'Text',
example: `nlp('it was the worst of times').match('worst').replaceWith('blurst').all().out()
//it was the blurst of times`
example: 'nlp(\'it was the worst of times\').match(\'worst\').replaceWith(\'blurst\').all().out()\n//it was the blurst of times'
},

@@ -181,4 +161,3 @@ replace: {

returns: 'Text',
example: `nlp('trust me folks, big league.').replace('big league','bigly').all().out()
//trust me folks, bigly.`
example: 'nlp(\'trust me folks, big league.\').replace(\'big league\',\'bigly\').all().out()\n//trust me folks, bigly.'
},

@@ -188,5 +167,4 @@ delete: {

returns: 'Text',
example: `nlp('you don’t win friends with salad').delete('do not').out()
//you win friends with salad`
},
example: 'nlp(\'you don\u2019t win friends with salad\').delete(\'do not\').out()\n//you win friends with salad'
}
},

@@ -198,4 +176,3 @@

returns: 'Text',
example: `nlp('we understand, we are from the land of chocolate.').match('land of #Noun').out()
//land of chocolate`
example: 'nlp(\'we understand, we are from the land of chocolate.\').match(\'land of #Noun\').out()\n//land of chocolate'
},

@@ -205,4 +182,3 @@ not: {

returns: 'Text',
example: `nlp('wait, there’s a new mexico?').places().not('new').out()
//mexico`
example: 'nlp(\'wait, there\u2019s a new mexico?\').places().not(\'new\').out()\n//mexico'
},

@@ -212,4 +188,3 @@ if: {

returns: 'Text',
example: `nlp('We’re here, we’re clear, we don’t want anymore bears.').clauses().if('anymore').out()
//we don't want anymore bears`
example: 'nlp(\'We\u2019re here, we\u2019re clear, we don\u2019t want anymore bears.\').clauses().if(\'anymore\').out()\n//we don\'t want anymore bears'
},

@@ -219,9 +194,6 @@ ifNo: {

returns: 'Text',
example: `nlp('We’re here, we’re clear, we don’t want anymore bears.').clauses().ifNo('anymore').out()
//We're here, we're clear,`
},
example: 'nlp(\'We\u2019re here, we\u2019re clear, we don\u2019t want anymore bears.\').clauses().ifNo(\'anymore\').out()\n//We\'re here, we\'re clear,'
}
},
split: {

@@ -231,4 +203,3 @@ splitOn: {

returns: 'Text',
example: `nlp('Monorail...Once again! Monorail... Monorail!').splitOn('monorail').get(0).out()
//Monorail`
example: 'nlp(\'Monorail...Once again! Monorail... Monorail!\').splitOn(\'monorail\').get(0).out()\n//Monorail'
},

@@ -238,4 +209,3 @@ splitBefore: {

returns: 'Text',
example: `nlp('Monorail...Once again! Monorail... Monorail!').splitBefore('monorail').get(0).out()
//Monorail...Once again!`
example: 'nlp(\'Monorail...Once again! Monorail... Monorail!\').splitBefore(\'monorail\').get(0).out()\n//Monorail...Once again!'
},

@@ -245,5 +215,4 @@ splitAfter: {

returns: 'Text',
example: `nlp('Monorail...Once again! Monorail... Monorail!').splitAfter('monorail').get(0).out()
//Monorail`
},
example: 'nlp(\'Monorail...Once again! Monorail... Monorail!\').splitAfter(\'monorail\').get(0).out()\n//Monorail'
}
},

@@ -255,4 +224,3 @@

returns: 'Text',
example: `nlp('Michael Apple ate a delicious apple.').match('#FirstName apple').tag('Person').people().out()
//Michael Apple`
example: 'nlp(\'Michael Apple ate a delicious apple.\').match(\'#FirstName apple\').tag(\'Person\').people().out()\n//Michael Apple'
},

@@ -262,4 +230,3 @@ unTag: {

returns: 'Text',
example: `nlp('they made a catch & scored a run').match(['run','catch']).unTag('#Verb').nouns().out('array')
//catch, run`
example: 'nlp(\'they made a catch & scored a run\').match([\'run\',\'catch\']).unTag(\'#Verb\').nouns().out(\'array\')\n//catch, run'
},

@@ -269,6 +236,5 @@ canBe: {

returns: 'Text',
example: `nlp('it’s fusilli jerry!').canBe('Person').out()
//fusilli jerry`
example: 'nlp(\'it\u2019s fusilli jerry!\').canBe(\'Person\').out()\n//fusilli jerry'
}
}
};

17

docs/subsets/contractions.js

@@ -0,6 +1,7 @@

'use strict';
module.exports = {
data: {
desc: 'return a handy array of meta-data for this the contractions in this text',
example: `nlp(' I’d like to request seventeen dollars for a push broom rebristling').contractions().data()
//[{text:'I'd'}]`,
example: 'nlp(\' I\u2019d like to request seventeen dollars for a push broom rebristling\').contractions().data()\n//[{text:\'I\'d\'}]',
returns: 'array'

@@ -11,4 +12,3 @@ },

returns: 'Text',
example: `nlp('He’s the greatest guy in history').contractions().expand().out('')
//He is`
example: 'nlp(\'He\u2019s the greatest guy in history\').contractions().expand().out(\'\')\n//He is'
},

@@ -18,4 +18,3 @@ contract: {

returns: 'Text',
example: `nlp('He is about to hit a chestnut tree').contractions().contract().out('')
//He's`
example: 'nlp(\'He is about to hit a chestnut tree\').contractions().contract().out(\'\')\n//He\'s'
},

@@ -25,4 +24,3 @@ contracted: {

returns: 'Text',
example: `nlp('Lisa, I’d like to buy your rock.').contractions().contracted().out('')
//I'd`
example: 'nlp(\'Lisa, I\u2019d like to buy your rock.\').contractions().contracted().out(\'\')\n//I\'d'
},

@@ -32,5 +30,4 @@ expanded: {

returns: 'Text',
example: `nlp('Lisa, I would like to buy your rock.').contractions().expanded().out('')
//I would`
example: 'nlp(\'Lisa, I would like to buy your rock.\').contractions().expanded().out(\'\')\n//I would'
}
};

12

docs/subsets/dates.js

@@ -0,6 +1,6 @@

'use strict';
module.exports = {
data: {
desc: 'return an array of meta-data about the dates and times in this text',
example: `nlp('Finally, I just stopped caring. Luckily for me, it was 1980 and no one noticed.').dates().data()
//[{text:'1980'}]`,
example: 'nlp(\'Finally, I just stopped caring. Luckily for me, it was 1980 and no one noticed.\').dates().data()\n//[{text:\'1980\'}]',
returns: 'array'

@@ -11,4 +11,3 @@ },

returns: 'Text',
example: `nlp('April, June, and Sept').dates().toShortForm().all().out()
//Apr, Jun, and Sept`
example: 'nlp(\'April, June, and Sept\').dates().toShortForm().all().out()\n//Apr, Jun, and Sept'
},

@@ -18,5 +17,4 @@ toLongForm: {

returns: 'Text',
example: `nlp('April, June, and Sept').dates().toShortForm().all().out()
//April, June, and September`
},
example: 'nlp(\'April, June, and Sept\').dates().toShortForm().all().out()\n//April, June, and September'
}
};

50

docs/subsets/index.js

@@ -0,1 +1,3 @@

'use strict';
module.exports = {

@@ -14,4 +16,3 @@ contractions: require('./contractions'),

desc: 'return an array of meta-data for the acronyms in this text',
example: `nlp('In the USA, the big CIA. The Bloods and the Crips, and the KKK.').acronyms().data()
//[{text:'USA'...}]`,
example: 'nlp(\'In the USA, the big CIA. The Bloods and the Crips, and the KKK.\').acronyms().data()\n//[{text:\'USA\'...}]',
returns: 'array'

@@ -24,4 +25,3 @@ }

desc: 'return an array of meta-data for the adjectives and their adverbs',
example: `nlp('Guys, we have to assume our guns are useless. Throw them in the lake.').adjectives().data()
//[{text:'useless'}]`,
example: 'nlp(\'Guys, we have to assume our guns are useless. Throw them in the lake.\').adjectives().data()\n//[{text:\'useless\'}]',
returns: 'array'

@@ -34,4 +34,3 @@ }

desc: 'return an array of meta-data for the adverbs in this text',
example: `nlp('truly, madly, deeply').adverbs().data()
//[{text:'truly'...}]`,
example: 'nlp(\'truly, madly, deeply\').adverbs().data()\n//[{text:\'truly\'...}]',
returns: 'array'

@@ -44,4 +43,3 @@ }

desc: 'return an array of words split by sentence phrase (clause)',
example: `nlp('All right, Colossus, you’re free to go, but stay away from Death Mountain').clauses().data()
//[{normal:'all right'}, {normal:'Colossus'}, {normal:'you're free to go'},]`,
example: 'nlp(\'All right, Colossus, you\u2019re free to go, but stay away from Death Mountain\').clauses().data()\n//[{normal:\'all right\'}, {normal:\'Colossus\'}, {normal:\'you\'re free to go\'},]',
returns: 'array'

@@ -54,4 +52,3 @@ }

desc: 'return an array of parsed hashtags used in the text',
example: `nlp('oh, but where is the #anykey').hashTags().data()
//[{normal:'anykey'}]`,
example: 'nlp(\'oh, but where is the #anykey\').hashTags().data()\n//[{normal:\'anykey\'}]',
returns: 'array'

@@ -64,4 +61,3 @@ }

desc: 'return an array of subsequences beginning at the start of each sentence or match',
example: `nlp('Who controls the British crown? Who keeps the metric system down?').startGrams().data()
//[{normal:'who', size:1, count:2}...]`,
example: 'nlp(\'Who controls the British crown? Who keeps the metric system down?\').startGrams().data()\n//[{normal:\'who\', size:1, count:2}...]',
returns: 'array'

@@ -73,4 +69,3 @@ }

desc: 'return an array of subsequences ending at the end of each sentence or match',
example: `nlp('you think that’s a knife? I’ll show you a knife.').endGrams().data()
//[{normal:'knife', count:2, size:1}...]`,
example: 'nlp(\'you think that\u2019s a knife? I\u2019ll show you a knife.\').endGrams().data()\n//[{normal:\'knife\', count:2, size:1}...]',
returns: 'array'

@@ -83,4 +78,3 @@ }

desc: 'return an array of named-organizations in this text',
example: `nlp('Your dreams may vary from those of Globex Corporation, its subsidiaries and shareholders.').organizations().data()
//[{text:'Globex Corporation'}]`,
example: 'nlp(\'Your dreams may vary from those of Globex Corporation, its subsidiaries and shareholders.\').organizations().data()\n//[{text:\'Globex Corporation\'}]',
returns: 'array'

@@ -93,4 +87,3 @@ }

desc: 'return an array of parsed phonenumbers in this text',
example: `nlp('Moe Sizlak. That’s right. I’m a surgeon. (800) 555-0000.').phoneNumbers().data()
//[{text:'(800) 555-0000'}]`,
example: 'nlp(\'Moe Sizlak. That\u2019s right. I\u2019m a surgeon. (800) 555-0000.\').phoneNumbers().data()\n//[{text:\'(800) 555-0000\'}]',
returns: 'array'

@@ -103,4 +96,3 @@ }

desc: 'return an array of locations mentioned in this text',
example: `nlp('you could still go to McGill, the Harvard of Canada!').places().data()
//[{normal:'canada'}]`,
example: 'nlp(\'you could still go to McGill, the Harvard of Canada!\').places().data()\n//[{normal:\'canada\'}]',
returns: 'array'

@@ -113,4 +105,3 @@ }

desc: 'return an array of question sentences in this text',
example: `nlp('are you saying boo, or boo-urns?').questions().data().length
//1`,
example: 'nlp(\'are you saying boo, or boo-urns?\').questions().data().length\n//1',
returns: 'array'

@@ -123,4 +114,3 @@ }

desc: 'return an array of meta-data with the parsed quoations',
example: `nlp('the he said "crazy like a fox!".').quotations().data().length
//1`,
example: 'nlp(\'the he said "crazy like a fox!".\').quotations().data().length\n//1',
returns: 'array'

@@ -133,4 +123,3 @@ }

desc: 'return an array of sentences that are not questions',
example: `nlp('i was saying boo-urns.').statements().data()
//[{normal:'i was saying boo-urns'}]`,
example: 'nlp(\'i was saying boo-urns.\').statements().data()\n//[{normal:\'i was saying boo-urns\'}]',
returns: 'array'

@@ -143,4 +132,3 @@ }

desc: 'split all words into individual results, and return their metadata',
example: `nlp('we should all be more like little Ruttiger').terms().data()
//[{text:'we'}, {text:'should'}...]`,
example: 'nlp(\'we should all be more like little Ruttiger\').terms().data()\n//[{text:\'we\'}, {text:\'should\'}...]',
returns: 'array'

@@ -153,4 +141,3 @@ }

desc: 'return the people, places, and organizations of this text',
example: `nlp('Hey everybody, I’m lookin’ for Amanda Hugginkiss').topics().data()
//[{text:'Amanda Hugginkiss'}]`,
example: 'nlp(\'Hey everybody, I\u2019m lookin\u2019 for Amanda Hugginkiss\').topics().data()\n//[{text:\'Amanda Hugginkiss\'}]',
returns: 'array'

@@ -163,4 +150,3 @@ }

desc: 'return an array of urls mentioned in this text',
example: `nlp('👏 http://simpsons.wikia.com').urls().data()
//[{text:'http://simpsons.wikia.com'}]`,
example: 'nlp(\'\uD83D\uDC4F http://simpsons.wikia.com\').urls().data()\n//[{text:\'http://simpsons.wikia.com\'}]',
returns: 'array'

@@ -167,0 +153,0 @@ }

19

docs/subsets/ngrams.js

@@ -0,6 +1,7 @@

'use strict';
module.exports = {
data: {
desc: 'return a handy array of meta-data for the n-grams in this text',
example: `nlp('love love me do.').ngrams().data()
//[{text:'love', count:2, size:1}..]`,
example: 'nlp(\'love love me do.\').ngrams().data()\n//[{text:\'love\', count:2, size:1}..]',
returns: 'array'

@@ -11,4 +12,3 @@ },

returns: 'Text',
example: `nlp('University of Toronto, in toronto').ngrams().unigrams(0).data()
//[{normal:'toronto', count:2, size:1}]`
example: 'nlp(\'University of Toronto, in toronto\').ngrams().unigrams(0).data()\n//[{normal:\'toronto\', count:2, size:1}]'
},

@@ -18,4 +18,3 @@ bigrams: {

returns: 'Text',
example: `nlp('The University of Ryerson and University of Toronto, in toronto').ngrams().bigrams(0).data()
//[{normal:'university of', count:2, size:2}]`
example: 'nlp(\'The University of Ryerson and University of Toronto, in toronto\').ngrams().bigrams(0).data()\n//[{normal:\'university of\', count:2, size:2}]'
},

@@ -25,4 +24,3 @@ trigrams: {

returns: 'Text',
example: `nlp('we like Roy! we like Roy!').ngrams().trigrams(0).data()
//[{normal:'we like roy', count:2, size:3}]`
example: 'nlp(\'we like Roy! we like Roy!\').ngrams().trigrams(0).data()\n//[{normal:\'we like roy\', count:2, size:3}]'
},

@@ -32,5 +30,4 @@ sort: {

returns: 'Text',
example: `nlp('i scream, you scream, we all scream for icecream.').ngrams().sort().first().out()
//scream`
},
example: 'nlp(\'i scream, you scream, we all scream for icecream.\').ngrams().sort().first().out()\n//scream'
}
};

19

docs/subsets/nouns.js

@@ -0,6 +1,7 @@

'use strict';
module.exports = {
data: {
desc: 'return a handy array of meta-data for the nouns in this text',
example: `nlp('Lunchlady Doris, have you got any grease?').nouns().data()
//[{text:'Lunchlady Doris'}, {text:'grease'}]`,
example: 'nlp(\'Lunchlady Doris, have you got any grease?\').nouns().data()\n//[{text:\'Lunchlady Doris\'}, {text:\'grease\'}]',
returns: 'array'

@@ -11,4 +12,3 @@ },

returns: 'Text',
example: `nlp('All my life I’ve had one dream, to accomplish my many goals.').nouns().isPlural().out()
//goals`
example: 'nlp(\'All my life I\u2019ve had one dream, to accomplish my many goals.\').nouns().isPlural().out()\n//goals'
},

@@ -18,4 +18,3 @@ hasPlural: {

returns: 'Text',
example: `nlp('bring joy to the air, water, earth, and sky.').nouns().hasPlural().length()
//0`
example: 'nlp(\'bring joy to the air, water, earth, and sky.\').nouns().hasPlural().length()\n//0'
},

@@ -25,4 +24,3 @@ toPlural: {

returns: 'Text',
example: `nlp('the purple dinosaur').nouns().toPlural().all().out()
//the purple dinosaurs`
example: 'nlp(\'the purple dinosaur\').nouns().toPlural().all().out()\n//the purple dinosaurs'
},

@@ -32,6 +30,5 @@ toSingular: {

returns: 'Text',
example: `nlp('the king’s men').nouns().toSingular().out()
//the king's man`
},
example: 'nlp(\'the king\u2019s men\').nouns().toSingular().out()\n//the king\'s man'
}
};

7

docs/subsets/people.js

@@ -0,6 +1,6 @@

'use strict';
module.exports = {
data: {
desc: 'return a handy array of meta-data of people mentioned in the text',
example: `nlp('The bong-rattling bass of Mel Schacher? The competent drum work of Don Brewer?').people().data()
//[{text:' Mel Schacher'}, {text:'Don Brewer'}]`,
example: 'nlp(\'The bong-rattling bass of Mel Schacher? The competent drum work of Don Brewer?\').people().data()\n//[{text:\' Mel Schacher\'}, {text:\'Don Brewer\'}]',
returns: 'array'

@@ -11,5 +11,4 @@ },

returns: 'String',
example: `nlp('Tony Hawk did a 900').people().pronoun()
//'he'`
example: 'nlp(\'Tony Hawk did a 900\').people().pronoun()\n//\'he\''
}
};

38

docs/subsets/sentences.js

@@ -0,6 +1,7 @@

'use strict';
module.exports = {
data: {
desc: 'return a handy array of meta-data for the sentences in this text',
example: `nlp('hi everybody! Hi Dr. Nick!').sentences().data().length
//[{normal:'hi everybody'}, {normal:'hi dr nick'}]`,
example: 'nlp(\'hi everybody! Hi Dr. Nick!\').sentences().data().length\n//[{normal:\'hi everybody\'}, {normal:\'hi dr nick\'}]',
returns: 'array'

@@ -11,4 +12,3 @@ },

returns: 'Text',
example: `nlp('I know so many words.').sentences().prepend('believe me').out()
//Believe me, I know so many words.`
example: 'nlp(\'I know so many words.\').sentences().prepend(\'believe me\').out()\n//Believe me, I know so many words.'
},

@@ -18,4 +18,3 @@ append: {

returns: 'Text',
example: `nlp('I know so many words.').sentences().append('big league').out()
//I know so many words big league.`
example: 'nlp(\'I know so many words.\').sentences().append(\'big league\').out()\n//I know so many words big league.'
},

@@ -25,4 +24,3 @@ toPastTense: {

returns: 'Text',
example: `nlp('I pay the Homer tax.').sentences().toPastTense().out()
//I paid the Homer tax.`
example: 'nlp(\'I pay the Homer tax.\').sentences().toPastTense().out()\n//I paid the Homer tax.'
},

@@ -32,4 +30,3 @@ toPresentTense: {

returns: 'Text',
example: `nlp('I paid the Homer tax.').sentences().toPresentTense().out()
//I pay the Homer tax.`
example: 'nlp(\'I paid the Homer tax.\').sentences().toPresentTense().out()\n//I pay the Homer tax.'
},

@@ -39,4 +36,3 @@ toFutureTense: {

returns: 'Text',
example: `nlp('That’s the home-owner tax.').sentences().toFutureTense().out()
//That will be the home-owner tax`
example: 'nlp(\'That\u2019s the home-owner tax.\').sentences().toFutureTense().out()\n//That will be the home-owner tax'
},

@@ -46,4 +42,3 @@ toNegative: {

returns: 'Text',
example: `nlp('Now make like my pants, and split.').sentences(0).toNegative().out()
//Now do not make like my pants, and split.`
example: 'nlp(\'Now make like my pants, and split.\').sentences(0).toNegative().out()\n//Now do not make like my pants, and split.'
},

@@ -53,4 +48,3 @@ toPositive: {

returns: 'Text',
example: `nlp('The goggles do nothing!').sentences().toPositive().out()
//The goggles do everything!`
example: 'nlp(\'The goggles do nothing!\').sentences().toPositive().out()\n//The goggles do everything!'
},

@@ -60,4 +54,3 @@ isPassive: {

returns: 'Text',
example: `nlp('you were saved by the bell').sentences().isPassive().out()
//you were saved by the bell`
example: 'nlp(\'you were saved by the bell\').sentences().isPassive().out()\n//you were saved by the bell'
},

@@ -67,4 +60,3 @@ toExclamation: {

returns: 'Text',
example: `nlp('sweet balls of fire?').sentences().toExclamation().out()
//sweet balls of fire!`
example: 'nlp(\'sweet balls of fire?\').sentences().toExclamation().out()\n//sweet balls of fire!'
},

@@ -74,4 +66,3 @@ toQuestion: {

returns: 'Text',
example: `nlp('Stupider like a fox.').sentences().toQuestion().out()
//Stupider like a fox?`
example: 'nlp(\'Stupider like a fox.\').sentences().toQuestion().out()\n//Stupider like a fox?'
},

@@ -81,5 +72,4 @@ toStatement: {

returns: 'Text',
example: `nlp('Go out on a Tuesday? Who am I, Charlie Sheen?').sentences(0).toStatement().out()
//Go out on a Tuesday. Who am I, Charlie Sheen?`
example: 'nlp(\'Go out on a Tuesday? Who am I, Charlie Sheen?\').sentences(0).toStatement().out()\n//Go out on a Tuesday. Who am I, Charlie Sheen?'
}
};

28

docs/subsets/values.js

@@ -0,6 +1,7 @@

'use strict';
module.exports = {
data: {
desc: 'return a handy array of meta-data for the numbers and values in this text',
example: `nlp('I’d like to request seventeen dollars for a push broom rebristling').values().data()
//[{number:17, TextValue:{ Ordinal:'seventeenth'}, NumericValue:{ Ordinal:'17th'}} ]`,
example: 'nlp(\'I\u2019d like to request seventeen dollars for a push broom rebristling\').values().data()\n//[{number:17, TextValue:{ Ordinal:\'seventeenth\'}, NumericValue:{ Ordinal:\'17th\'}} ]',
returns: 'array'

@@ -11,4 +12,3 @@ },

returns: 'Text',
example: `nlp('in 2016, I’m going to party like it’s on sale for $19.99.').values().noDates().length
//1`
example: 'nlp(\'in 2016, I\u2019m going to party like it\u2019s on sale for $19.99.\').values().noDates().length\n//1'
},

@@ -18,4 +18,3 @@ toNumber: {

returns: 'Text',
example: `nlp('ten things i hate about you').values().toNumber().all().out()
//10 things i hate about you`
example: 'nlp(\'ten things i hate about you\').values().toNumber().all().out()\n//10 things i hate about you'
},

@@ -25,4 +24,3 @@ toTextValue: {

returns: 'Text',
example: `nlp('10 things i hate about you').values().toTextValue().all().out()
//ten things i hate about you`
example: 'nlp(\'10 things i hate about you\').values().toTextValue().all().out()\n//ten things i hate about you'
},

@@ -32,4 +30,3 @@ toNiceNumber: {

returns: 'Text',
example: `nlp('five hundred sixty two thousand, four hundred and seven').values().toTextValue().all().out()
//'562,407'`
example: 'nlp(\'five hundred sixty two thousand, four hundred and seven\').values().toTextValue().all().out()\n//\'562,407\''
},

@@ -39,4 +36,3 @@ toCardinal: {

returns: 'Text',
example: `nlp('twenty-third of december').values().toCardinal().all().out()
//23rd of december`
example: 'nlp(\'twenty-third of december\').values().toCardinal().all().out()\n//23rd of december'
},

@@ -46,4 +42,3 @@ toOrdinal: {

returns: 'Text',
example: `nlp('three strikes').values().toOrdinal().all().nouns().toSingular().all().out()
//third strike`
example: 'nlp(\'three strikes\').values().toOrdinal().all().nouns().toSingular().all().out()\n//third strike'
},

@@ -53,5 +48,4 @@ numbers: {

returns: 'Array',
example: `nlp('at the seven eleven').values().numbers()
// [7, 11]`
},
example: 'nlp(\'at the seven eleven\').values().numbers()\n// [7, 11]'
}
};

54

docs/subsets/verbs.js

@@ -0,7 +1,7 @@

'use strict';
module.exports = {
data: {
desc: 'return a handy array of meta-data for the verbs and their auxillaries in this text',
example: `nlp('he will have been walking quickly').verbs().data()
//[{verb:'walking', auxillary:'will have been', adverb:'quickly'}]
`,
example: 'nlp(\'he will have been walking quickly\').verbs().data()\n//[{verb:\'walking\', auxillary:\'will have been\', adverb:\'quickly\'}]\n',
returns: 'array'

@@ -12,5 +12,3 @@ },

returns: 'String',
example: `nlp('My cat’s breath smells like cat food').verbs().conjugation()
//['PresentTense']
`
example: 'nlp(\'My cat\u2019s breath smells like cat food\').verbs().conjugation()\n//[\'PresentTense\']\n'
},

@@ -20,5 +18,3 @@ conjugate: {

returns: 'Array',
example: `nlp('she walked the walk').verbs().conjugate()
//[{Infinitive:'walk', ...}]
`
example: 'nlp(\'she walked the walk\').verbs().conjugate()\n//[{Infinitive:\'walk\', ...}]\n'
},

@@ -29,5 +25,3 @@

returns: 'Text',
example: `nlp('we were discussing Wittgenstein over a game of backgammon').verbs().isSingular().out('array')
//[{text:'were discussing'}]
`
example: 'nlp(\'we were discussing Wittgenstein over a game of backgammon\').verbs().isSingular().out(\'array\')\n//[{text:\'were discussing\'}]\n'
},

@@ -37,5 +31,3 @@ isPlural: {

returns: 'Text',
example: `nlp('We were sitting in Barney’s car').verbs().isPlural().out('array')
//[]
`
example: 'nlp(\'We were sitting in Barney\u2019s car\').verbs().isPlural().out(\'array\')\n//[]\n'
},

@@ -46,5 +38,3 @@

returns: 'Text',
example: `nlp('Dear Miss Hoover, you have Lyme disease.').verbs().isNegative().length()
//1
`
example: 'nlp(\'Dear Miss Hoover, you have Lyme disease.\').verbs().isNegative().length()\n //1\n '
},

@@ -54,5 +44,3 @@ isNegative: {

returns: 'Text',
example: `nlp('Dear Miss Hoover, you have Lyme disease.').verbs().isNegative().length()
//0
`
example: 'nlp(\'Dear Miss Hoover, you have Lyme disease.\').verbs().isNegative().length()\n//0\n'
},

@@ -62,5 +50,3 @@ toNegative: {

returns: 'Text',
example: `nlp('Dear Miss Hoover, you have Lyme disease.').verbs().toNegative().out()
//do not have
`
example: 'nlp(\'Dear Miss Hoover, you have Lyme disease.\').verbs().toNegative().out()\n//do not have\n'
},

@@ -70,5 +56,3 @@ toPositive: {

returns: 'Text',
example: `nlp('Dear Miss Hoover, you do not have Lyme disease').verbs().toPositive().out()
//have
`
example: 'nlp(\'Dear Miss Hoover, you do not have Lyme disease\').verbs().toPositive().out()\n//have\n'
},

@@ -78,5 +62,3 @@ toPastTense: {

returns: 'Text',
example: `nlp('It tastes like burning.').verbs().toPastTense().out()
//tasted
`
example: 'nlp(\'It tastes like burning.\').verbs().toPastTense().out()\n//tasted\n'
},

@@ -86,5 +68,3 @@ toPresentTense: {

returns: 'Text',
example: `nlp('you ate the sandwich?').verbs().toPresentTense().out()
//you eat the sandwich?
`
example: 'nlp(\'you ate the sandwich?\').verbs().toPresentTense().out()\n//you eat the sandwich?\n'
},

@@ -94,4 +74,3 @@ toFutureTense: {

returns: 'Text',
example: `nlp('I’m a furniture! ').verbs().toFutureTense().out()
//will be`
example: 'nlp(\'I\u2019m a furniture! \').verbs().toFutureTense().out()\n//will be'
},

@@ -101,5 +80,4 @@ asAdjective: {

returns: 'Array',
example: `nlp('strain').verbs().asAdjective()
//strenuous`
},
example: 'nlp(\'strain\').verbs().asAdjective()\n//strenuous'
}
};

2

package.json

@@ -5,3 +5,3 @@ {

"description": "natural language processing in the browser",
"version": "7.0.16",
"version": "7.0.17",
"main": "./builds/compromise.js",

@@ -8,0 +8,0 @@ "repository": {

builds/compromise.es6.min.js

Sorry, the diff of this file is too big to display

builds/compromise.js

Sorry, the diff of this file is too big to display

builds/compromise.min.js

Sorry, the diff of this file is too big to display

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