As a helpful and smart assistant, your task is to parse a given text in any language to Universal Dependency (UD) format including POS tags and dependency relations (HEAD and DEPREL).
Key Components of UD Annotation:
1. Tokens: The fundamental units within UD are tokens. These can be individual words, punctuation marks, or even parts of multi-word expressions. Each token has several associated attributes.
2. UPOS Tags: The UPOS field in the CoNLL-U format represents the universal part-of-speech tag assigned to each word. This tag is part of a universal POS tag set designed to be applicable across different languages. The UPOS tags include categories such as NOUN, VERB, ADJ (adjective), ADV (adverb), PRON (pronoun), DET (determiner), and more. 
3. Dependency Relations: Dependency relations are the core of the syntactic representation. They form a tree structure over the tokens in a sentence. Here's how they work:
    - HEAD: This field indicates the head of the current word. The head is the word that the current word is connected to in the dependency parse tree. The value is an integer that corresponds to the ID of the head word within the same sentence. A value of zero (0) indicates that the word is the root of the sentence.
    - DEPREL: The DEPREL field specifies the type of dependency relation between the current word and its head (as specified in the HEAD field). The value is a universal dependency relation or a language-specific subtype of such a relation.
Note: Blank lines are used to mark the boundaries between sentences.
Example:
Let's analyze this sentence with its UD annotation:
"The big dog chased the cat."
1. Tokenization:
- The
- big
- dog
- chased
- the
- cat
- .
2. UPOS Tags:
- The: DET
- big: ADJ
- dog: NOUN
- chased: VERB
- the: DET
- cat: NOUN
- .: PUNCT
3. Head node (parent):
- The: the parent of this word is "dog" at position 3
- big: the parent of this word is "dog" at position 3
- dog: the parent of this word is "chased" at position 4
- chased: this is the root node, therefore its head is 0
- the: the parent of this word is "cat" at position 6
- cat: the parent of this word is "chased" at position 4
- .: the parent of this word is "chased" at position 4
3. Dependency Relations:
- root(ROOT, chased)
- det(dog, The)
- amod(dog, big)
- nsubj(chased, dog)
- det(cat, the)
- obj(chased, cat)
Combining the above annotations, the final output in UD format is:
Position	Tokenization	UPOS	Head	Deprel
1	The	DET	3	det
2	big	ADJ	3	amod
3	dog	NOUN	4	nsubj
4	chased	VERB	0	ROOT
5	the	DET	6	det
6	cat	NOUN	4	obj
7	.	PUNCT	4	punct
Generate UD annotations for the following tokenized text:
Quién sabe si, al final, acabará cumpliéndose la profecía del premio Nobel de física estadounidense Isidor Isaac Rabi: - La combinación de técnica militar y ciencia hace fácil aplicar ambas para destruir al ser humano, ya que en realidad no se trata de una estructura biológica muy fuerte.
Postion	Tokenization
1	Quién
2	sabe
3	si
4	,
5	al
6	final
7	,
8	acabará
9	cumpliéndose
10	cumpliendo
11	se
12	la
13	profecía
14	del
15	premio
16	Nobel
17	de
18	física
19	estadounidense
20	Isidor
21	Isaac
22	Rabi
23	:
24	-
25	La
26	combinación
27	de
28	técnica
29	militar
30	y
31	ciencia
32	hace
33	fácil
34	aplicar
35	ambas
36	para
37	destruir
38	al
39	ser
40	humano
41	,
42	ya
43	que
44	en
45	realidad
46	no
47	se
48	trata
49	de
50	una
51	estructura
52	biológica
53	muy
54	fuerte
55	.
Position	Tokenization	UPOS	Head	Deprel
1	Quién	PRON	2	nsubj
2	sabe	VERB	0	root
3	si	SCONJ	8	mark
4	,	PUNCT	5	punct
5	al	ADP	8	advmod
6	final	NOUN	5	fixed
7	,	PUNCT	5	punct
8	acabará	VERB	2	ccomp
9	cumpliéndose	_	None	_
10	cumpliendo	VERB	8	xcomp
11	se	PRON	9	expl:pass
12	la	DET	12	det
13	profecía	NOUN	8	nsubj
14	del	ADP	14	case
15	premio	NOUN	12	nmod
16	Nobel	PROPN	14	appos
17	de	ADP	17	case
18	física	NOUN	14	nmod
19	estadounidense	ADJ	14	amod
20	Isidor	PROPN	14	appos
21	Isaac	PROPN	19	flat
22	Rabi	PROPN	19	flat
23	:	PUNCT	31	punct
24	-	PUNCT	31	punct
25	La	DET	25	det
26	combinación	NOUN	31	nsubj
27	de	ADP	27	case
28	técnica	NOUN	25	nmod
29	militar	ADJ	27	amod
30	y	CCONJ	30	cc
31	ciencia	NOUN	27	conj
32	hace	VERB	12	acl
33	fácil	ADJ	31	iobj
34	aplicar	VERB	31	xcomp
35	ambas	NUM	33	obj
36	para	ADP	36	mark
37	destruir	VERB	33	advcl
38	al	ADP	38	case
39	ser	NOUN	36	obj
40	humano	ADJ	38	amod
41	,	PUNCT	47	punct
42	ya	ADV	47	mark
43	que	SCONJ	41	fixed
44	en	ADP	47	advmod
45	realidad	NOUN	43	fixed
46	no	ADV	47	advmod
47	se	PRON	47	expl:impers
48	trata	VERB	31	advcl
49	de	ADP	50	case
50	una	DET	50	det
51	estructura	NOUN	47	obj
52	biológica	ADJ	50	amod
53	muy	ADV	53	advmod
54	fuerte	ADJ	50	amod
55	.	PUNCT	2	punct