Upstream spencermountain/compromise state of the touched files before c7e26e9613
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@@ -0,0 +1,18 @@
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const keep = { tags: true }
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const nounToSingular = function (m, parsed) {
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// already singular?
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if (parsed.isPlural === false) {
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return m
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}
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const { methods, model } = m.world
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const { toSingular } = methods.two.transform.noun
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// inflect the root noun
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const str = parsed.root.text('normal')
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const single = toSingular(str, model)
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m.replace(parsed.root, single, keep).tag('Singular', 'toPlural')
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// should we change the determiner/article?
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// m.debug()
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return m
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}
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export default nounToSingular
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@@ -0,0 +1,259 @@
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import test from 'tape'
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import nlp from '../_lib.js'
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const here = '[three/noun-toSingular] '
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test('toSingular:', function (t) {
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const arr = [
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// ["Joneses", "Jones"],
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['agents', 'agent'],
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['aircraft', 'aircraft'],
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['airports', 'airport'],
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['analyses', 'analysis'],
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['anchors', 'anchor'],
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['anchovies', 'anchovy'],
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['apples', 'apple'],
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['apricots', 'apricot'],
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['bags', 'bag'],
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['balls', 'ball'],
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['bananas', 'banana'],
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['barracks', 'barracks'],
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['bases', 'base'],
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['bass', 'bass'],
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['bats', 'bat'],
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['bears', 'bear'],
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['beds', 'bed'],
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['bells', 'bell'],
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['bicycles', 'bicycle'],
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['bison', 'bison'],
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['blankets', 'blanket'],
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['blocks', 'block'],
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['boats', 'boat'],
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['bonuses', 'bonus'],
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['books', 'book'],
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['bottles', 'bottle'],
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['bricks', 'brick'],
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['brooms', 'broom'],
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['bubbles', 'bubble'],
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['bunnies', 'bunny'],
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['buses', 'bus'],
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['butterflies', 'butterfly'],
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['buttons', 'button'],
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['cacti', 'cactus'],
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['cakes', 'cake'],
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['calves', 'calf'],
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['campuses', 'campus'],
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['candles', 'candle'],
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['capes', 'cape'],
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['carrots', 'carrot'],
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['cars', 'car'],
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['cases', 'case'],
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['cats', 'cat'],
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['causes', 'cause'],
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['chairs', 'chair'],
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['cheese', 'cheese'],
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['cheetahs', 'cheetah'],
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['chickens', 'chicken'],
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['children', 'child'],
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['clauses', 'clause'],
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['clocks', 'clock'],
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['clouds', 'cloud'],
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['cookies', 'cookie'],
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['cords', 'cord'],
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['cowboys', 'cowboy'],
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['cows', 'cow'],
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['crabs', 'crab'],
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['crayons', 'crayon'],
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['crises', 'crisis'],
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['cupcakes', 'cupcake'],
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['decreases', 'decrease'],
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['deer', 'deer'],
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['desks', 'desk'],
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['dinosaurs', 'dinosaur'],
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['diseases', 'disease'],
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['dogs', 'dog'],
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['donuts', 'donut'],
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['doorways', 'doorway'],
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['ducks', 'duck'],
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['eagles', 'eagle'],
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['embryos', 'embryo'],
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['eyebrows', 'eyebrow'],
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['fans', 'fan'],
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['fences', 'fence'],
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['fishes', 'fish'],
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['fountains', 'fountain'],
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['fowl', 'fowl'],
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['foxes', 'fox'],
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['frames', 'frame'],
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['frescos', 'fresco'],
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['fungi', 'fungus'],
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['geese', 'goose'],
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['ghettos', 'ghetto'],
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['glasses', 'glass'],
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['gloves', 'glove'],
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['grapes', 'grape'],
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['groceries', 'grocery'],
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['guns', 'gun'],
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['halos', 'halo'],
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['hammers', 'hammer'],
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['hens', 'hen'],
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['hippos', 'hippo'],
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['his excuses', 'his excuse'],
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['hooves', 'hoof'],
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['houses', 'house'],
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['increases', 'increase'],
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['jackets', 'jacket'],
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['kangaroos', 'kangaroo'],
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['keys', 'key'],
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['kilos', 'kilo'],
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['kimonos', 'kimono'],
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['kites', 'kite'],
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['kittens', 'kitten'],
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['knives', 'knife'],
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['laptops', 'laptop'],
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['lizards', 'lizard'],
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['loaves', 'loaf'],
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['locks', 'lock'],
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['logos', 'logo'],
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['lollipops', 'lollipop'],
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['mangos', 'mango'],
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['mannequins', 'mannequin'],
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['masks', 'mask'],
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['mementos', 'memento'],
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['memos', 'memo'],
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['men', 'man'],
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['mice', 'mouse'],
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['mirrors', 'mirror'],
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['monitors', 'monitor'],
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['monkeys', 'monkey'],
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['mottos', 'motto'],
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['mountains', 'mountain'],
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['mugs', 'mug'],
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['music', 'music'],
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['needles', 'needle'],
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['nerds', 'nerd'],
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['nuclei', 'nucleus'],
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['oafs', 'oaf'],
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['oceans', 'ocean'],
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['olives', 'olive'],
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['oranges', 'orange'],
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['owls', 'owl'],
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['paintings', 'painting'],
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['parentheses', 'parenthesis'],
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['pearls', 'pearl'],
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['penguins', 'penguin'],
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['pens', 'pen'],
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['people', 'person'],
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['phenomena', 'phenomenon'],
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['photos', 'photo'],
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['phrases', 'phrase'],
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['pianos', 'piano'],
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['pies', 'pie'],
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['pigs', 'pig'],
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['pillars', 'pillar'],
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['pillows', 'pillow'],
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['pimentos', 'pimento'],
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['plants', 'plant'],
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['plates', 'plate'],
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['pleases', 'please'],
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['ponchos', 'poncho'],
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['posters', 'poster'],
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['prognoses', 'prognosis'],
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['pros', 'pro'],
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['puzzles', 'puzzle'],
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['quintuplets', 'quintuplet'],
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['rabbits', 'rabbit'],
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['reeds', 'reed'],
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['releases', 'release'],
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['rivers', 'river'],
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['robots', 'robot'],
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['rocks', 'rock'],
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['roofs', 'roof'],
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['roses', 'rose'],
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['scarves', 'scarf'],
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['seeds', 'seed'],
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['sharks', 'shark'],
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['sheep', 'sheep'],
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['shoes', 'shoe'],
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['snails', 'snail'],
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['sofas', 'sofa'],
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['sombreros', 'sombrero'],
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['spiders', 'spider'],
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['spoons', 'spoon'],
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['spouses', 'spouse'],
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['stairs', 'stair'],
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['sticks', 'stick'],
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['surpluses', 'surplus'],
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['swords', 'sword'],
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['syllabi', 'syllabus'],
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['synopses', 'synopsis'],
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['tables', 'table'],
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['tacos', 'taco'],
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['teapots', 'teapot'],
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['teeth', 'tooth'],
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['tents', 'tent'],
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['the focuses', 'the focus'],
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['the geniouses', 'the genious'],
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['theses', 'thesis'],
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['tigers', 'tiger'],
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['tomatoes', 'tomato'],
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['tornados', 'tornado'],
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['torsos', 'torso'],
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['tosses', 'toss'],
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['toys', 'toy'],
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['trees', 'tree'],
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['tuxedos', 'tuxedo'],
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['uses', 'use'],
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['viruses', 'virus'],
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['walruses', 'walrus'],
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['whales', 'whale'],
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['wishes', 'wish'],
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['wives', 'wife'],
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['wolves', 'wolf'],
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['women', 'woman'],
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['worrywarts', 'worrywart'],
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['xylophones', 'xylophone'],
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['zebras', 'zebra'],
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[`ireland potatoes`, 'ireland potato'],
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[`irish potatoes`, 'irish potato'],
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[`john's potatoes`, `john's potato`],
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// ['Isley Brothers members', 'Isley Brothers member'],
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// ['mayors of chicago', 'mayor of chicago'],
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//test that sungular.singularize()==singular..
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]
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arr.forEach(function (a) {
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let doc = nlp(a[0])
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doc.tag('Noun').compute('chunks')
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let str = doc.nouns().toSingular().text()
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t.equal(str, a[1], here + '[toSingular] ' + a[0])
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doc = nlp(a[1])
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doc.tag('Noun').compute('chunks')
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str = doc.nouns().toPlural().text()
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t.equal(str, a[0], here + '[toPlural] ' + a[0])
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// doc = nlp(a[0])
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// doc.tag('Noun').compute('chunks')
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// str = doc.nouns().toPlural().text()
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// t.equal(str, a[0], here + '[stay-plural] ' + a[0])
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})
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t.end()
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})
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test('toSingular - longer:', function (t) {
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const arr = [
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[`my fingers looked green afterwards`, `my finger looked green afterwards`],
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[`other person`, 'other person'],
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[`other people`, 'other person'],
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// [`other people's kids`, 'other person\'s kid'],
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// [`the women's room`, 'the women\'s room'],
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]
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arr.forEach(function (a) {
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const doc = nlp(a[0])
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doc.compute('chunks')
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doc.nouns().toSingular()
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t.equal(doc.text(), a[1], here + '[longer] ' + a[0])
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})
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t.end()
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})
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