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:
En 1987 se instaló con una beca Fulbright en Nueva York, donde además de conocer revistas de distinto pelaje se casó con la puertorriqueña Marel Malaret, con la que ha tenido dos hijos, Amalia, de 8 años, y Mario, de 3, y que ha sido cómplice e infatigable apoyo.
Postion	Tokenization
1	En
2	1987
3	se
4	instaló
5	con
6	una
7	beca
8	Fulbright
9	en
10	Nueva
11	York
12	,
13	donde
14	además
15	de
16	conocer
17	revistas
18	de
19	distinto
20	pelaje
21	se
22	casó
23	con
24	la
25	puertorriqueña
26	Marel
27	Malaret
28	,
29	con
30	la
31	que
32	ha
33	tenido
34	dos
35	hijos
36	,
37	Amalia
38	,
39	de
40	8
41	años
42	,
43	y
44	Mario
45	,
46	de
47	3
48	,
49	y
50	que
51	ha
52	sido
53	cómplice
54	e
55	infatigable
56	apoyo
57	.
Position	Tokenization	UPOS	Head	Deprel
1	En	ADP	2	case
2	1987	NOUN	4	obl
3	se	PRON	4	expl:pv
4	instaló	VERB	0	root
5	con	ADP	7	case
6	una	DET	7	det
7	beca	NOUN	4	obl
8	Fulbright	PROPN	7	appos
9	en	ADP	10	case
10	Nueva	PROPN	4	obl
11	York	PROPN	10	flat
12	,	PUNCT	22	punct
13	donde	PRON	22	obl
14	además	ADV	16	mark
15	de	ADP	14	fixed
16	conocer	VERB	22	advcl
17	revistas	NOUN	16	obj
18	de	ADP	20	case
19	distinto	ADJ	20	amod
20	pelaje	NOUN	17	nmod
21	se	PRON	22	expl:pv
22	casó	VERB	10	acl
23	con	ADP	25	case
24	la	DET	25	det
25	puertorriqueña	NOUN	22	obl
26	Marel	PROPN	25	appos
27	Malaret	PROPN	26	flat
28	,	PUNCT	33	punct
29	con	ADP	31	case
30	la	DET	31	det
31	que	PRON	33	obl
32	ha	AUX	33	aux
33	tenido	VERB	25	acl
34	dos	NUM	35	nummod
35	hijos	NOUN	33	obj
36	,	PUNCT	37	punct
37	Amalia	PROPN	35	appos
38	,	PUNCT	41	punct
39	de	ADP	41	case
40	8	NUM	41	nummod
41	años	NOUN	37	nmod
42	,	PUNCT	41	punct
43	y	CCONJ	44	cc
44	Mario	PROPN	37	conj
45	,	PUNCT	47	punct
46	de	ADP	47	case
47	3	NUM	44	nummod
48	,	PUNCT	53	punct
49	y	CCONJ	53	cc
50	que	PRON	53	nsubj
51	ha	AUX	53	aux
52	sido	AUX	53	cop
53	cómplice	NOUN	33	conj
54	e	CCONJ	56	cc
55	infatigable	ADJ	56	amod
56	apoyo	NOUN	53	conj
57	.	PUNCT	4	punct