Olympiad: thinking like a machine, Bebras puzzles ★★★, sentence 7

A simple next-word guesser counts which words came after a word in its text and picks the one it saw most often.

Who acts: simple word guesser, words, guesser · What happens: count, come, pick, see · To what, where: text, one

This is a sentence of the lesson «Olympiad: thinking like a machine, Bebras puzzles ★★★» of the ai course of Mova's primary school. The page gives its syntax tree — a dependency tree in the style of Universal Dependencies — and its pragmatics clause by clause: who acts (simple word guesser, words, guesser), what happens, when and where, as Mova's frames read it from the tree.

The tree

The words, one row each (CoNLL-U)

#	word	lemma	UPOS	features	head	relation
1	A	a	DET	Definite=Ind PronType=Art	6 guesser	det
2	simple	simple	ADJ	Degree=Pos	6 guesser	amod
3	next	next	ADJ	Degree=Pos	5 word	amod
4	-	-	PUNCT	_	5 word	punct
5	word	word	NOUN	Number=Sing	6 guesser	compound
6	guesser	guesser	NOUN	Number=Sing	7 counts	nsubj
7	counts	count	VERB	Mood=Ind Number=Sing Person=3 Tense=Pres VerbForm=Fin	root	root
8	which	which	DET	PronType=Rel	9 words	det
9	words	word	NOUN	Number=Plur	10 came	nsubj
10	came	come	VERB	Mood=Ind Number=Plur Person=3 Tense=Past VerbForm=Fin	7 counts	ccomp
11	after	after	ADP	_	13 word	case
12	a	a	DET	Definite=Ind PronType=Art	13 word	det
13	word	word	NOUN	Number=Sing	10 came	obl
14	in	in	ADP	_	16 text	case
15	its	its	PRON	Case=Gen Gender=Neut Number=Sing Person=3 Poss=Yes PronType=Prs	16 text	nmod:poss
16	text	text	NOUN	Number=Sing	10 came	obl
17	and	and	CCONJ	_	18 picks	cc
18	picks	pick	VERB	Mood=Ind Number=Sing Person=3 Tense=Pres VerbForm=Fin	10 came	conj
19	the	the	DET	Definite=Def PronType=Art	20 one	det
20	one	one	NOUN	Number=Sing	18 picks	obj
21	it	it	PRON	Case=Nom Gender=Neut Number=Sing Person=3 PronType=Prs	22 saw	nsubj
22	saw	see	VERB	Mood=Ind Number=Sing Person=3 Tense=Past VerbForm=Fin	20 one	acl:relcl
23	most	most	ADV	Degree=Sup	24 often	advmod
24	often	often	ADV	_	22 saw	advmod
25	.	.	PUNCT	_	7 counts	punct

Pragmatics

No stored speech act for this sentence.

#	verb	tense, mood	who	what, whom	where	when	other	roles by
1	count	present	a simple word guesser					vn characterize-29.2 «NP V NP to be ADJ»: agent=Agent clause=Attribute
2	come	past	which words		word's text	after a word		vn appear-48.1.1 «NP V PP.location»: agent=Theme in text=Location
3	pick	present	which words not said here	the one				vn chew-39.2 «NP V NP»: agent=Agent one=Patient
4	see	past	guesser	the one not said here			often property	vn comprehend-87.2-1-1 «NP V NP»: agent=Experiencer one=Stimulus

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.

Licence: CC BY-SA 4.0, as the whole of Mova corpus 0.4, of which this treebank (the agreed school, mova-school-agreed-en.conllu) is part. The lessons were written for Mova's primary school. Sentence school:ai:031-olympiad-thinking-like-a-machine.lesson:7.

The tree: Mova annot: Rust (en) → Haiku 5.5 (medium, Batches API) patches; gates UD 2.18; agreed by treeagree rules (Stanza, Trankit, spaCy trf): R1-cc-next ×1, R6-punct-agree ×1. The frames are read from the tree by Mova's frames reader (prag) when this page is built, the lesson read in order, so a pronoun may be resolved from an earlier sentence.

See the release 0.4.

This page in Markdown → https://md.mova.elamur.ai/free/en/corpora/school/ai/31-olympiad-thinking-like-a-machine/7/