---
title: Large studies found MSG (a powder added to food to make it taste sav… — How would we know? Asking experts and spotting t… · tree and pragmatics
description: Large studies found MSG (a powder added to food to make it taste savoury) safe for most people in normal amounts; tomatoes and cheese have natural glutamate too, and old fears about it were partly mixed with unfair attitudes to Chinese food. Who acts: Large studies, MSG, tomatoes. A sentence of the 
---

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# How would we know? Asking experts and spotting tricks, sentence 11

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Large studies found MSG (a powder added to food to make it taste savoury) safe for most people in normal amounts; tomatoes and cheese have natural glutamate too, and old fears about it were partly mixed with unfair attitudes to Chinese food.

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**Who acts:** Large studies, MSG, tomatoes · **What happens:** find, add, make, taste, have, mix · **To what, where:** MSG, powder, natural glutamate, old fears

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This is a sentence of the lesson «How would we know? Asking experts and spotting tricks» of the myths 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 (Large studies, MSG, tomatoes), what happens, when and where, as Mova's frames read it from the tree.

## The tree {#tree}

```conllu
# sent_id = school:myths:030-how-would-we-know-asking-experts-and-spotting-tricks.lesson:11
# text = Large studies found MSG (a powder added to food to make it taste savoury) safe for most people in normal amounts; tomatoes and cheese have natural glutamate too, and old fears about it were partly mixed with unfair attitudes to Chinese food.
# source = school lesson (ours)
# course = myths
# lesson = 030-how-would-we-know-asking-experts-and-spotting-tricks.lesson
# annotation = 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, R6-punct-agree ×1, R7-rel-agree ×1
1	Large	large	ADJ	JJ	Degree=Pos	2	amod	_	_
2	studies	study	NOUN	NNS	Number=Plur	3	nsubj	_	_
3	found	find	VERB	VBD	Mood=Ind|Number=Plur|Person=3|Tense=Past|VerbForm=Fin	0	root	_	_
4	MSG	msg	NOUN	NN	Number=Sing	3	obj	_	_
5	(	(	PUNCT	-LRB-	_	7	punct	_	_
6	a	a	DET	DT	Definite=Ind|PronType=Art	7	det	_	_
7	powder	powder	NOUN	NN	Number=Sing	4	appos	_	_
8	added	add	VERB	VBN	Tense=Past|VerbForm=Part|Voice=Pass	7	acl	_	_
9	to	to	ADP	IN	_	10	case	_	_
10	food	food	NOUN	NN	Number=Sing	8	obl	_	_
11	to	to	PART	TO	_	12	mark	_	_
12	make	make	VERB	VB	VerbForm=Inf	8	advcl	_	_
13	it	it	PRON	PRP	Case=Acc|Gender=Neut|Number=Sing|Person=3|PronType=Prs	12	obj	_	_
14	taste	taste	VERB	VB	VerbForm=Inf	12	xcomp	_	_
15	savoury	savoury	ADJ	JJ	Degree=Pos	14	xcomp	_	_
16	)	)	PUNCT	-RRB-	_	7	punct	_	_
17	safe	safe	ADJ	JJ	Degree=Pos	3	xcomp	_	_
18	for	for	ADP	IN	_	20	case	_	_
19	most	most	ADJ	JJS	Degree=Sup	20	amod	_	_
20	people	people	NOUN	NNS	Number=Plur	17	obl	_	_
21	in	in	ADP	IN	_	23	case	_	_
22	normal	normal	ADJ	JJ	Degree=Pos	23	amod	_	_
23	amounts	amount	NOUN	NNS	Number=Plur	17	obl	_	Agree=R5-strong|Haiku=20:nmod
24	;	;	PUNCT	,	_	25	punct	_	_
25	tomatoes	tomato	NOUN	NNS	Number=Plur	28	nsubj	_	_
26	and	and	CCONJ	CC	_	27	cc	_	_
27	cheese	cheese	NOUN	NN	Number=Sing	25	conj	_	_
28	have	have	VERB	VBP	Mood=Ind|Number=Plur|Person=3|Tense=Pres|VerbForm=Fin	3	parataxis	_	Agree=R7-rel-agree|Haiku=3:conj
29	natural	natural	ADJ	JJ	Degree=Pos	30	amod	_	_
30	glutamate	glutamate	NOUN	NN	Number=Sing	28	obj	_	_
31	too	too	ADV	RB	_	28	advmod	_	_
32	,	,	PUNCT	,	_	40	punct	_	Agree=R6-punct-agree|Haiku=35:punct
33	and	and	CCONJ	CC	_	35	cc	_	_
34	old	old	ADJ	JJ	Degree=Pos	35	amod	_	_
35	fears	fear	NOUN	NNS	Number=Plur	40	nsubj:pass	_	_
36	about	about	ADP	IN	_	37	case	_	_
37	it	it	PRON	PRP	Case=Acc|Gender=Neut|Number=Sing|Person=3|PronType=Prs	35	nmod	_	_
38	were	be	AUX	VBD	Mood=Ind|Number=Sing|Person=3|Tense=Past|VerbForm=Fin	40	aux:pass	_	_
39	partly	partly	ADV	RB	_	40	advmod	_	_
40	mixed	mix	VERB	VBN	Tense=Past|VerbForm=Part|Voice=Pass	28	conj	_	_
41	with	with	ADP	IN	_	43	case	_	_
42	unfair	unfair	ADJ	JJ	Degree=Pos	43	amod	_	_
43	attitudes	attitude	NOUN	NNS	Number=Plur	40	obl	_	_
44	to	to	ADP	IN	_	46	case	_	_
45	Chinese	Chinese	ADJ	JJ	Degree=Pos	46	amod	_	_
46	food	food	NOUN	NN	Number=Sing	43	nmod	_	_
47	.	.	PUNCT	.	_	3	punct	_	_
```

## Pragmatics {#pragmatics}

<!-- sa -->
- form: statement
- act: assert
- voice: narrator
- polarity: none
- teacher: kept the draft

<!-- .fr -->
| # | verb | tense, mood | who | what, whom | to whom | where | when | other | roles by |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1 | **find** | past | Large studies | MSG |  |  |  | safe *property* | vn discover-84 «NP V NP»: agent=Agent msg=Topic |
| 2 | **add** *speech: assert* | participle |  | a powder *not said here* |  |  |  | to food | vn mix-22.1-2 «NP V ADVP-Middle PP»: agent=Patient to food=Co-Patient |
| 3 | **make** | to-infinitive |  | MSG |  |  |  |  | tree rules |
| 4 | **taste** | infinitive | MSG *not said here* |  |  |  |  | savoury *property* | vn stimulus\_subject-30.4 «NP V ADJ»: agent=Stimulus |
| 5 | **have** | present | tomatoes and cheese | natural glutamate |  |  |  |  | vn birth-28.2-1 «NP V NP»: agent=Agent glutamate=Patient |
| 6 | **mix** | past · passive |  | old fears about food |  |  |  | with unfair attitudes to Chinese food; partly *property* | vn mix-22.1-1-1 «NP V NP PP.co-patient»: subject=Patient with attitude=Co-Patient |

<!-- .note -->
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.

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**Licence: [CC BY-SA 4.0](https://creativecommons.org/licenses/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:myths:030-how-would-we-know-asking-experts-and-spotting-tricks.lesson:11`.

**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, R6-punct-agree ×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](https://md.mova.elamur.ai/free/en/releases/0.4/).

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