Upstream spencermountain/compromise state of the touched files before 53d5f9abeb

src/3-three/sentences/parse/mainClause.js

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+// if a clause starts with these, it's not a main clause
+const subordinate = `(after|although|as|because|before|if|since|than|that|though|when|whenever|where|whereas|wherever|whether|while|why|unless|until|once)`
+const relative = `(that|which|whichever|who|whoever|whom|whose|whomever)`
+
+//try to remove secondary clauses
+const mainClause = function (s) {
+  let m = s
+  if (m.length === 1) {
+    return m
+  }
+  // if there's no verb, it's dependent
+  m = m.if('#Verb')
+  if (m.length === 1) {
+    return m
+  }
+  // this is a signal for subordinate-clauses
+  m = m.ifNo(subordinate)
+  m = m.ifNo('^even (if|though)')
+  m = m.ifNo('^so that')
+  m = m.ifNo('^rather than')
+  m = m.ifNo('^provided that')
+  if (m.length === 1) {
+    return m
+  }
+  // relative clauses
+  m = m.ifNo(relative)
+  if (m.length === 1) {
+    return m
+  }
+
+  // check for subordinating conjunctions -- must be at the beginning of the clause
+  m = m.ifNo('(^despite|^during|^before|^through|^throughout)')
+  if (m.length === 1) {
+    return m
+  }
+
+  // check for clauses beginning with Gerund ("Taking ..., ...")
+  m = m.ifNo('^#Gerund')
+  if (m.length === 1) {
+    return m
+  }
+
+  // did we go too far?
+  if (m.length === 0) {
+    m = s
+  }
+  // choose the first one?
+  return m.eq(0)
+}
+export default mainClause

tests/three/sentences/svo.test.js

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+import test from 'tape'
+import nlp from '../_lib.js'
+const here = '[three/svo] '
+
+test('svo parser', function (t) {
+  const arr = [
+    ['My dog loves pizza crusts.', 'my dog', 'loves'],
+    ['My grey dog loves pizza crusts.', 'my grey dog', 'loves'],
+    ['if i can recall, my grey dog loves pizza crusts.', 'my grey dog', 'loves'],
+    ['I remember what you said yesterday.', 'i', 'remember'],
+    ['i had suddenly and quickly arrived in Kyiev', 'i', 'had suddenly and quickly arrived'],
+    ['I eat bananas in the kitchen.', 'i', 'eat'],
+    ['I thought what she wore was so chic.', 'i', 'thought'],
+    ['In the kitchen, I eat', 'i', 'eat'],
+    ['smoked poutine is delicious', 'smoked poutine', 'is'],
+    [`by chance, the tape was discovered in the room`, `the tape`, `was discovered`],
+    [`every year, we gather in Baltimore`, `we`, `gather`],
+    [`if only i was awake, we could have eaten the breakfast`, `we`, `could have eaten`],
+    ['Every night before I go to bed, I eat bananas.', 'i', 'eat'],
+    [
+      'Diane decided to plant tomatoes in the back of the yard, where the sun blazed the longest during the day.',
+      'diane',
+      'decided to plant',
+    ],
+    ['Once Adam smashed the spider, he ran into the bathroom', 'he', 'ran into'],
+    [`We are introducing ourselves to the class.`, 'we', 'are introducing ourselves'],
+    [
+      'Throughout the day, as the sun gradually set, the temperature dropped significantly, creating a chilly evening.',
+      'the temperature',
+      'dropped significantly',
+    ],
+    [
+      'He collaborated with the university to develop comprehensive academic proposals throughout the entire campus, encompassing academic priorities in every department at the university and course structure typologies for each academic program.',
+      'he',
+      'collaborated',
+    ],
+    ['Despite Susan walking the dog, she was not wearing a coat.', 'she', 'was not wearing'],
+    [
+      'Taking diligent notes throughout the entire class, the students remained focused during the lecture.',
+      'the students',
+      'remained',
+    ],
+
+    // ['every day the kitten tries to eat the mouse', 'the kitten', 'tries to eat'],
+    // ['The boy who you saw at the store committed a robbery.', 'the boy', 'committed'],
+    // ['After dripping mustard all over his chest, the man who was wearing a red shirt wished that he had instead chosen ketchup for his hotdog.', 'the man', 'wished'],
+    // ["please tell me you'll address the issue", 'you', 'will address'],
+    // ['The store that the boy robbed is on the corner.', 'the store', 'is'],
+  ]
+  arr.forEach(a => {
+    const doc = nlp(a[0])
+    const obj = doc.sentences().json()[0]
+    t.equal(obj.sentence.subject, a[1], here + 'subject: ' + a[1])
+    t.equal(obj.sentence.verb, a[2], here + 'verb: ' + a[2])
+  })
+  t.end()
+})