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 español Pedro de la Rosa ha sido el autor de los mejores tiempos en las pruebas previas al inicio del Campeonato del Mundo de Fórmula Uno al volante del nuevo Arrows A21 Supertec, con el que en dos ocasiones pulverizó el récord del circuito de Cataluña.
Postion	Tokenization
1	El
2	español
3	Pedro
4	de
5	la
6	Rosa
7	ha
8	sido
9	el
10	autor
11	de
12	los
13	mejores
14	tiempos
15	en
16	las
17	pruebas
18	previas
19	al
20	inicio
21	del
22	Campeonato
23	del
24	Mundo
25	de
26	Fórmula
27	Uno
28	al
29	volante
30	del
31	nuevo
32	Arrows
33	A21
34	Supertec
35	,
36	con
37	el
38	que
39	en
40	dos
41	ocasiones
42	pulverizó
43	el
44	récord
45	del
46	circuito
47	de
48	Cataluña
49	.
Position	Tokenization	UPOS	Head	Deprel
1	El	DET	2	det
2	español	NOUN	10	nsubj
3	Pedro	PROPN	2	appos
4	de	ADP	6	case
5	la	DET	6	det
6	Rosa	PROPN	3	flat
7	ha	AUX	10	aux
8	sido	AUX	10	cop
9	el	DET	10	det
10	autor	NOUN	0	root
11	de	ADP	14	case
12	los	DET	14	det
13	mejores	ADJ	14	amod
14	tiempos	NOUN	10	nmod
15	en	ADP	17	case
16	las	DET	17	det
17	pruebas	NOUN	10	obl
18	previas	ADJ	17	amod
19	al	ADP	20	case
20	inicio	NOUN	18	nmod
21	del	ADP	22	case
22	Campeonato	PROPN	20	nmod
23	del	ADP	24	case
24	Mundo	PROPN	22	flat
25	de	ADP	26	case
26	Fórmula	PROPN	22	flat
27	Uno	PROPN	26	flat
28	al	ADP	29	case
29	volante	NOUN	10	obl
30	del	ADP	32	case
31	nuevo	ADJ	32	amod
32	Arrows	PROPN	29	nmod
33	A21	PROPN	32	flat
34	Supertec	PROPN	32	flat
35	,	PUNCT	42	punct
36	con	ADP	38	case
37	el	DET	38	det
38	que	PRON	42	obl
39	en	ADP	41	case
40	dos	NUM	41	nummod
41	ocasiones	NOUN	42	obl
42	pulverizó	VERB	32	acl
43	el	DET	44	det
44	récord	NOUN	42	obj
45	del	ADP	46	case
46	circuito	NOUN	44	nmod
47	de	ADP	48	case
48	Cataluña	PROPN	46	nmod
49	.	PUNCT	10	punct