correlation Who acts: things, measure, positive correlation, graph, temperature, coat sales · What happens: change, go up, show, measure, be, rise · To what, where: scatter graph, two sets, negative correlation, other, correlation · Speech: someone speaks here
«correlation» in the school dictionary: its 2 meanings for a pupil and its 5 examples, every sentence parsed by Mova itself — its syntax tree, a dependency tree in the style of Universal Dependencies, and its pragmatics clause by clause. The word in the dictionary, with its sense in the lessons: correlation in the school dictionary .
Meaning 1 A connection or relationship between two things, where they change together.
det cc conj case nummod nmod punct advmod nsubj acl:relcl advmod punct root A DET connection NOUN or CCONJ relationship NOUN between ADP two NUM things NOUN , PUNCT where ADV they PRON change VERB together ADV . PUNCT The words, one row each (CoNLL-U) # word lemma UPOS features head relation 1 A a DET Definite=Ind PronType=Art 2 connection det 2 connection connection NOUN Number=Sing root root 3 or or CCONJ _ 4 relationship cc 4 relationship relationship NOUN Number=Sing 2 connection conj 5 between between ADP _ 7 things case 6 two two NUM NumForm=Word NumType=Card 7 things nummod 7 things thing NOUN Number=Plur 4 relationship nmod 8 , , PUNCT _ 11 change punct 9 where where ADV PronType=Rel 11 change advmod 10 they they PRON Case=Nom Number=Plur Person=3 PronType=Prs 11 change nsubj 11 change change VERB Mood=Ind Number=Plur Person=3 Tense=Pres VerbForm=Fin 7 things acl:relcl 12 together together ADV _ 11 change advmod 13 . . PUNCT _ 2 connection punct
# verb tense, mood who what, whom to whom where when other roles by
1 change present things = thing together property vn dress-41.1.1-1-1 «NP V»: agent=Agent
One row per clause the reader finds. «not said here»: a role filled from the clause above. «roles by»: a VerbNet class and frame when one fits the verb, else the reader's rules on the tree.
Meaning 2 Usage: maths, science — kept apart, not parsed.
A measure of how closely two sets of numbers go up or down together, as shown on a scatter graph.
The tree, 20 words det nsubj case advmod advmod nummod nmod case nmod advmod cc conj advmod punct mark conj case det amod obl punct root A DET measure NOUN of ADP how ADV closely ADV two NUM sets NOUN of ADP numbers NOUN go VERB up ADV or CCONJ down ADV together ADV , PUNCT as SCONJ shown VERB on ADP a DET scatter ADJ graph NOUN . PUNCT The words, one row each (CoNLL-U) # word lemma UPOS features head relation 1 A a DET Definite=Ind PronType=Art 2 measure det 2 measure measure NOUN Number=Sing 10 go nsubj 3 of of ADP _ 7 sets case 4 how how ADV PronType=Int 5 closely advmod 5 closely closely ADV _ 6 two advmod 6 two two NUM NumForm=Word NumType=Card 7 sets nummod 7 sets set NOUN Number=Plur 2 measure nmod 8 of of ADP _ 9 numbers case 9 numbers number NOUN Number=Plur 7 sets nmod 10 go go VERB Mood=Ind Number=Plur Person=3 Tense=Pres VerbForm=Fin root root 11 up up ADV _ 10 go advmod 12 or or CCONJ _ 13 down cc 13 down down ADV _ 11 up conj 14 together together ADV _ 10 go advmod 15 , , PUNCT _ 17 shown punct 16 as as SCONJ _ 17 shown mark 17 shown show VERB Tense=Past VerbForm=Part Voice=Pass 10 go conj 18 on on ADP _ 21 graph case 19 a a DET Definite=Ind PronType=Art 21 graph det 20 scatter scat ADJ Degree=Cmp 21 graph amod 21 graph graph NOUN Number=Sing 17 shown obl 22 . . PUNCT _ 10 go punct
# verb tense, mood who what, whom to whom where when other roles by
1 go up present a measure of two sets of numbers together property vn calibratable_cos-45.6.1-1 «NP.attribute V»: agent=Attribute 2 show participle a measure of two sets of numbers not said here a scatter graph vn transfer_mesg-37.1.1-1-1 «NP V»: agent=Agent 3 measure two sets of numbers nom measure «act transitive»: of=patient
Example 1 There is a positive correlation between height and shoe size.
expl det amod nsubj case nmod cc compound conj punct root There PRON is VERB a DET positive ADJ correlation NOUN between ADP height NOUN and CCONJ shoe NOUN size NOUN . PUNCT The words, one row each (CoNLL-U) # word lemma UPOS features head relation 1 There there PRON PronType=Dem 2 is expl 2 is be VERB Mood=Ind Number=Sing Person=3 Tense=Pres VerbForm=Fin root root 3 a a DET Definite=Ind PronType=Art 5 correlation det 4 positive positive ADJ Degree=Pos 5 correlation amod 5 correlation correlation NOUN Number=Sing 2 is nsubj 6 between between ADP _ 7 height case 7 height height NOUN Number=Sing 5 correlation nmod 8 and and CCONJ _ 10 size cc 9 shoe shoe NOUN Number=Sing 10 size compound 10 size size NOUN Number=Sing 7 height conj 11 . . PUNCT _ 2 is punct
# verb tense, mood who what, whom to whom where when other roles by
1 be present a positive correlation between height and shoe size there vn exist-47.1 «NP V»: agent=Theme
Example 2 The graph shows a negative correlation: as temperature rises, coat sales fall.
The tree, 12 words det nsubj det amod obj punct mark nsubj conj punct compound nsubj advcl punct root The DET graph NOUN shows VERB a DET negative ADJ correlation NOUN : PUNCT as SCONJ temperature NOUN rises VERB , PUNCT coat NOUN sales NOUN fall VERB . PUNCT The words, one row each (CoNLL-U) # word lemma UPOS features head relation 1 The the DET Definite=Def PronType=Art 2 graph det 2 graph graph NOUN Number=Sing 3 shows nsubj 3 shows show VERB Mood=Ind Number=Sing Person=3 Tense=Pres VerbForm=Fin root root 4 a a DET Definite=Ind PronType=Art 6 correlation det 5 negative negative ADJ Degree=Pos 6 correlation amod 6 correlation correlation NOUN Number=Sing 3 shows obj 7 : : PUNCT _ 10 rises punct 8 as as SCONJ _ 10 rises mark 9 temperature temperature NOUN Number=Sing 10 rises nsubj 10 rises rise VERB Mood=Ind Number=Sing Person=3 Tense=Pres VerbForm=Fin 3 shows conj 11 , , PUNCT _ 14 fall punct 12 coat coat NOUN Number=Sing 13 sales compound 13 sales sale NOUN Number=Plur 14 fall nsubj 14 fall fall VERB Mood=Ind Number=Plur Person=3 Tense=Pres VerbForm=Fin 10 rises advcl 15 . . PUNCT _ 3 shows punct
# verb tense, mood who what, whom to whom where when other roles by
1 show present the graph a negative correlation vn transfer_mesg-37.1.1-1-1 «NP V NP.recipient»: agent=Agent correlation=Recipient 2 rise present temperature vn assuming_position-50 «NP V»: agent=Agent 3 fall present coat sales vn occur-48.3 «NP V»: agent=Theme
Example 3 There was no correlation between hair colour and test scores.
expl det nsubj case compound nmod cc compound conj punct root There PRON was VERB no DET correlation NOUN between ADP hair NOUN colour NOUN and CCONJ test NOUN scores NOUN . PUNCT The words, one row each (CoNLL-U) # word lemma UPOS features head relation 1 There there PRON PronType=Dem 2 was expl 2 was be VERB Mood=Ind Number=Sing Person=3 Tense=Past VerbForm=Fin root root 3 no no DET PronType=Neg 4 correlation det 4 correlation correlation NOUN Number=Sing 2 was nsubj 5 between between ADP _ 7 colour case 6 hair hair NOUN Number=Sing 7 colour compound 7 colour colour NOUN Number=Sing 4 correlation nmod 8 and and CCONJ _ 10 scores cc 9 test test NOUN Number=Sing 10 scores compound 10 scores score NOUN Number=Plur 7 colour conj 11 . . PUNCT _ 2 was punct
# verb tense, mood who what, whom to whom where when other roles by
1 be past no correlation between hair colour and test scores there vn exist-47.1 «NP V»: agent=Theme
Example 4 Correlation does not mean one thing causes the other.
nsubj aux advmod nummod nsubj ccomp det obj punct root Correlation NOUN does AUX not PART mean VERB one NUM thing NOUN causes VERB the DET other ADJ . PUNCT The words, one row each (CoNLL-U) # word lemma UPOS features head relation 1 Correlation correlation NOUN Number=Sing 4 mean nsubj 2 does do AUX Mood=Ind Number=Sing Person=3 Tense=Pres VerbForm=Fin 4 mean aux 3 not not PART Polarity=Neg 4 mean advmod 4 mean mean VERB VerbForm=Inf root root 5 one one NUM NumForm=Word NumType=Card 6 thing nummod 6 thing thing NOUN Number=Sing 7 causes nsubj 7 causes cause VERB Mood=Ind Number=Sing Person=3 Tense=Pres VerbForm=Fin 4 mean ccomp 8 the the DET Definite=Def PronType=Art 9 other det 9 other other ADJ Degree=Pos 7 causes obj 10 . . PUNCT _ 4 mean punct
# verb tense, mood who what, whom to whom where when other roles by
1 not mean present · do-support Correlation vn intend-61.2-1 «NP V S»: agent=Agent clause=Topic 2 cause present one thing the other vn engender-27.1-1 «NP V NP»: agent=Precondition other=Patient
Example 5 Scientists found a correlation between exercise and sleep.
nsubj det obj case nmod cc conj punct root Scientists NOUN found VERB a DET correlation NOUN between ADP exercise NOUN and CCONJ sleep NOUN . PUNCT The words, one row each (CoNLL-U) # word lemma UPOS features head relation 1 Scientists scientist NOUN Number=Plur 2 found nsubj 2 found find VERB Mood=Ind Number=Plur Person=3 Tense=Past VerbForm=Fin root root 3 a a DET Definite=Ind PronType=Art 4 correlation det 4 correlation correlation NOUN Number=Sing 2 found obj 5 between between ADP _ 6 exercise case 6 exercise exercise NOUN Number=Sing 4 correlation nmod 7 and and CCONJ _ 8 sleep cc 8 sleep sleep NOUN Number=Sing 6 exercise conj 9 . . PUNCT _ 2 found punct
# verb tense, mood who what, whom to whom where when other roles by
1 find past Scientists a correlation between exercise and sleep vn discover-84 «NP V NP»: agent=Agent correlation=Topic
Text: the school dictionary of Mova's schools (world/data/dictionary-schools/), written for Mova. Trees: Mova's own parse — the en model on the CPU, when the site is built — not checked by a teacher; they may be wrong. Frames: Mova's frames reader (prag), each meaning and example read on its own. Elamur Cosmic License.
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