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:
A las 17.30 horas el índice estadounidense Dow Jones ganaba un 0,71 por ciento y las principales bolsas europeas también cerraron con subidas, siendo la del mercado de Fráncfort el que más ganó, un 2,35 por ciento; seguido de Londres, con un 1,03 por ciento, mientras que París avanzaba un 0,74 por ciento.
Postion	Tokenization
1	A
2	las
3	17.30
4	horas
5	el
6	índice
7	estadounidense
8	Dow
9	Jones
10	ganaba
11	un
12	0,71
13	por
14	ciento
15	y
16	las
17	principales
18	bolsas
19	europeas
20	también
21	cerraron
22	con
23	subidas
24	,
25	siendo
26	la
27	del
28	mercado
29	de
30	Fráncfort
31	el
32	que
33	más
34	ganó
35	,
36	un
37	2,35
38	por
39	ciento
40	;
41	seguido
42	de
43	Londres
44	,
45	con
46	un
47	1,03
48	por
49	ciento
50	,
51	mientras
52	que
53	París
54	avanzaba
55	un
56	0,74
57	por
58	ciento
59	.
Position	Tokenization	UPOS	Head	Deprel
1	A	ADP	4	case
2	las	DET	3	det
3	17.30	NOUN	4	nmod
4	horas	NOUN	10	obl
5	el	DET	6	det
6	índice	NOUN	10	nsubj
7	estadounidense	ADJ	6	amod
8	Dow	PROPN	6	appos
9	Jones	PROPN	8	flat
10	ganaba	VERB	51	parataxis
11	un	DET	12	det
12	0,71	NUM	10	obj
13	por	ADP	14	case
14	ciento	NUM	12	compound
15	y	CCONJ	51	cc
16	las	DET	18	det
17	principales	ADJ	18	amod
18	bolsas	NOUN	21	nsubj
19	europeas	ADJ	18	amod
20	también	ADV	21	advmod
21	cerraron	VERB	51	parataxis
22	con	ADP	23	case
23	subidas	NOUN	21	obl
24	,	PUNCT	25	punct
25	siendo	AUX	21	advcl
26	la	DET	25	det
27	del	ADP	26	case
28	mercado	NOUN	26	nsubj
29	de	ADP	30	case
30	Fráncfort	PROPN	28	nmod
31	el	DET	34	det
32	que	PRON	34	nsubj
33	más	ADV	34	obj
34	ganó	VERB	25	ccomp
35	,	PUNCT	37	punct
36	un	DET	37	det
37	2,35	NUM	34	obj
38	por	ADP	39	case
39	ciento	NUM	37	compound
40	;	PUNCT	41	punct
41	seguido	ADJ	34	advmod
42	de	ADP	43	case
43	Londres	PROPN	41	obj
44	,	PUNCT	47	punct
45	con	ADP	47	case
46	un	DET	47	det
47	1,03	NUM	43	nummod
48	por	ADP	49	case
49	ciento	NUM	47	compound
50	,	PUNCT	47	punct
51	mientras	CCONJ	0	root
52	que	SCONJ	51	fixed
53	París	PROPN	54	nsubj
54	avanzaba	VERB	51	parataxis
55	un	DET	56	det
56	0,74	NUM	54	obj
57	por	ADP	58	case
58	ciento	NUM	56	compound
59	.	PUNCT	51	punct