How a language model learns: guessing the next word, sentence 14

A bot is more than a parrot, because it can mix ideas in new ways, but everything it knows came from what people made.

Who acts: bot, everything, people · What happens: be, mix, know, come, make · To what, where: ideas, new ways

This is a sentence of the lesson «How a language model learns: guessing the next word» 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 (bot, everything, people), 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	2 bot	det
2	bot	bot	NOUN	Number=Sing	4 more	nsubj
3	is	be	AUX	Mood=Ind Number=Sing Person=3 Tense=Pres VerbForm=Fin	4 more	cop
4	more	more	ADJ	Degree=Cmp	root	root
5	than	than	ADP	_	7 parrot	case
6	a	a	DET	Definite=Ind PronType=Art	7 parrot	det
7	parrot	parrot	NOUN	Number=Sing	4 more	obl
8	,	,	PUNCT	_	12 mix	punct
9	because	because	SCONJ	_	12 mix	mark
10	it	it	PRON	Case=Nom Gender=Neut Number=Sing Person=3 PronType=Prs	12 mix	nsubj
11	can	can	AUX	VerbForm=Fin	12 mix	aux
12	mix	mix	VERB	VerbForm=Inf	4 more	advcl
13	ideas	idea	NOUN	Number=Plur	12 mix	obj
14	in	in	ADP	_	16 ways	case
15	new	new	ADJ	Degree=Pos	16 ways	amod
16	ways	way	NOUN	Number=Plur	12 mix	obl
17	,	,	PUNCT	_	22 came	punct
18	but	but	CCONJ	_	22 came	cc
19	everything	everything	PRON	Number=Sing PronType=Tot	22 came	nsubj
20	it	it	PRON	Case=Nom Gender=Neut Number=Sing Person=3 PronType=Prs	21 knows	nsubj
21	knows	know	VERB	Mood=Ind Number=Sing Person=3 Tense=Pres VerbForm=Fin	19 everything	acl:relcl
22	came	come	VERB	Mood=Ind Number=Sing Person=3 Tense=Past VerbForm=Fin	4 more	conj
23	from	from	ADP	_	24 what	case
24	what	what	PRON	PronType=Rel	22 came	obl
25	people	people	NOUN	Number=Plur	26 made	nsubj
26	made	make	VERB	Mood=Ind Number=Plur Person=3 Tense=Past VerbForm=Fin	24 what	acl:relcl
27	.	.	PUNCT	_	4 more	punct

Pragmatics

No stored speech act for this sentence.

#	verb	tense, mood	who	what, whom	where	other	roles by
1	is	present	a bot			than a parrot; more property	tree rules
2	mix	can	bot	ideas	new ways		vn mix-22.1-1-1 «NP V NP»: agent=Agent idea=Patient
3	know	present	bot				vn comprehend-87.2-1-1-1 «NP V»: agent=Experiencer
4	come	past	everything				vn escape-51.1-1 «NP V PP.initial_location»: agent=Theme from what=Initial_Location
5	make	past	people				vn build-26.1-1 «NP V NP»: 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.

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:002-guessing-the-next-word.lesson:14.

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): R5-strong ×1, R7-rel-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/2-guessing-the-next-word/14/