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:
La entrada de turistas en Catalunya superará la cifra récord de 18 millones de personas en 1999, según las previsiones que ha realizado la Conselleria de Indústria ,Comerç i Turisme de la Generalitat, que calcula un incremento del 7% respecto a la cifra de visitantes de 1998.
Postion	Tokenization
1	La
2	entrada
3	de
4	turistas
5	en
6	Catalunya
7	superará
8	la
9	cifra
10	récord
11	de
12	18
13	millones
14	de
15	personas
16	en
17	1999
18	,
19	según
20	las
21	previsiones
22	que
23	ha
24	realizado
25	la
26	Conselleria
27	de
28	Indústria
29	,Comerç
30	i
31	Turisme
32	de
33	la
34	Generalitat
35	,
36	que
37	calcula
38	un
39	incremento
40	del
41	7%
42	respecto
43	a
44	la
45	cifra
46	de
47	visitantes
48	de
49	1998
50	.
Position	Tokenization	UPOS	Head	Deprel
1	La	DET	2	det
2	entrada	NOUN	7	nsubj
3	de	ADP	4	case
4	turistas	NOUN	2	nmod
5	en	ADP	6	case
6	Catalunya	PROPN	2	nmod
7	superará	VERB	0	root
8	la	DET	9	det
9	cifra	NOUN	7	obj
10	récord	ADJ	9	amod
11	de	ADP	13	case
12	18	NUM	13	nummod
13	millones	NOUN	9	nmod
14	de	ADP	15	case
15	personas	NOUN	13	nmod
16	en	ADP	17	case
17	1999	NOUN	7	obl
18	,	PUNCT	21	punct
19	según	ADP	21	case
20	las	DET	21	det
21	previsiones	NOUN	7	obl
22	que	PRON	24	obj
23	ha	AUX	24	aux
24	realizado	VERB	21	acl
25	la	DET	26	det
26	Conselleria	PROPN	24	nsubj
27	de	ADP	28	case
28	Indústria	PROPN	26	flat
29	,Comerç	PROPN	28	flat
30	i	CCONJ	31	cc
31	Turisme	PROPN	28	conj
32	de	ADP	34	case
33	la	DET	34	det
34	Generalitat	PROPN	26	flat
35	,	PUNCT	37	punct
36	que	PRON	37	nsubj
37	calcula	VERB	26	acl
38	un	DET	39	det
39	incremento	NOUN	37	obj
40	del	ADP	41	case
41	7%	SYM	39	nmod
42	respecto	NOUN	45	case
43	a	ADP	42	fixed
44	la	DET	45	det
45	cifra	NOUN	39	nmod
46	de	ADP	47	case
47	visitantes	NOUN	45	nmod
48	de	ADP	49	case
49	1998	NOUN	45	nmod
50	.	PUNCT	7	punct