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:
- El tenista brasileño Gustavo "Guga" Kuerten, que hoy regresó al lugar número uno del mundo, consiguió elevar mucho su nivel en los últimos meses, pero aún puede mejorar más, dijo el también brasileño Carlos Alberto Kirmair, ex entrenador de tenistas como Gabriela Sabatini y Conchita Martínez.
Postion	Tokenization
1	-
2	El
3	tenista
4	brasileño
5	Gustavo
6	"Guga"
7	Kuerten
8	,
9	que
10	hoy
11	regresó
12	al
13	lugar
14	número
15	uno
16	del
17	mundo
18	,
19	consiguió
20	elevar
21	mucho
22	su
23	nivel
24	en
25	los
26	últimos
27	meses
28	,
29	pero
30	aún
31	puede
32	mejorar
33	más
34	,
35	dijo
36	el
37	también
38	brasileño
39	Carlos
40	Alberto
41	Kirmair
42	,
43	ex
44	entrenador
45	de
46	tenistas
47	como
48	Gabriela
49	Sabatini
50	y
51	Conchita
52	Martínez
53	.
Position	Tokenization	UPOS	Head	Deprel
1	-	PUNCT	19	punct
2	El	DET	3	det
3	tenista	NOUN	19	nsubj
4	brasileño	ADJ	3	amod
5	Gustavo	PROPN	3	appos
6	"Guga"	PROPN	5	flat
7	Kuerten	PROPN	5	flat
8	,	PUNCT	11	punct
9	que	PRON	11	nsubj
10	hoy	ADV	11	advmod
11	regresó	VERB	3	acl
12	al	ADP	13	case
13	lugar	NOUN	11	obl
14	número	NOUN	13	nmod
15	uno	PRON	14	nmod
16	del	ADP	17	case
17	mundo	NOUN	14	nmod
18	,	PUNCT	11	punct
19	consiguió	VERB	0	root
20	elevar	VERB	19	xcomp
21	mucho	ADV	20	advmod
22	su	DET	23	det
23	nivel	NOUN	20	obj
24	en	ADP	27	case
25	los	DET	27	det
26	últimos	ADJ	27	amod
27	meses	NOUN	20	obl
28	,	PUNCT	35	punct
29	pero	CCONJ	35	cc
30	aún	ADV	32	advmod
31	puede	AUX	32	aux
32	mejorar	VERB	35	xcomp
33	más	ADV	32	advmod
34	,	PUNCT	32	punct
35	dijo	VERB	19	conj
36	el	DET	38	det
37	también	ADV	38	advmod
38	brasileño	NOUN	35	nsubj
39	Carlos	PROPN	38	appos
40	Alberto	PROPN	39	flat
41	Kirmair	PROPN	39	flat
42	,	PUNCT	44	punct
43	ex	ADJ	44	amod
44	entrenador	NOUN	38	appos
45	de	ADP	46	case
46	tenistas	NOUN	44	nmod
47	como	SCONJ	48	mark
48	Gabriela	PROPN	46	nmod
49	Sabatini	PROPN	48	flat
50	y	CCONJ	51	cc
51	Conchita	PROPN	48	conj
52	Martínez	PROPN	51	flat
53	.	PUNCT	19	punct