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 homenaje contará con la participación de casi un centenar de profesionales del espectáculo entre músicos - - Maria del Mar Bonet, Toti Soler, Oriol Tranvia - -, magos - - Hausson y Mag Lari - - y actores como Pavlovsky, Vicky Peña, Carme Sansa, Ferran Rañé y Feliu Formosa, entre otros.
Postion	Tokenization
1	El
2	homenaje
3	contará
4	con
5	la
6	participación
7	de
8	casi
9	un
10	centenar
11	de
12	profesionales
13	del
14	espectáculo
15	entre
16	músicos
17	-
18	-
19	Maria
20	del
21	Mar
22	Bonet
23	,
24	Toti
25	Soler
26	,
27	Oriol
28	Tranvia
29	-
30	-
31	,
32	magos
33	-
34	-
35	Hausson
36	y
37	Mag
38	Lari
39	-
40	-
41	y
42	actores
43	como
44	Pavlovsky
45	,
46	Vicky
47	Peña
48	,
49	Carme
50	Sansa
51	,
52	Ferran
53	Rañé
54	y
55	Feliu
56	Formosa
57	,
58	entre
59	otros
60	.
Position	Tokenization	UPOS	Head	Deprel
1	El	DET	2	det
2	homenaje	NOUN	3	nsubj
3	contará	VERB	0	root
4	con	ADP	6	case
5	la	DET	6	det
6	participación	NOUN	3	obj
7	de	ADP	12	case
8	casi	ADV	10	advmod
9	un	DET	10	det
10	centenar	NOUN	12	nmod
11	de	ADP	10	case
12	profesionales	NOUN	6	nmod
13	del	ADP	14	case
14	espectáculo	NOUN	12	nmod
15	entre	ADP	16	case
16	músicos	NOUN	12	nmod
17	-	PUNCT	19	punct
18	-	PUNCT	19	punct
19	Maria	PROPN	16	appos
20	del	ADP	21	case
21	Mar	PROPN	19	flat
22	Bonet	PROPN	21	flat
23	,	PUNCT	24	punct
24	Toti	PROPN	19	conj
25	Soler	PROPN	24	flat
26	,	PUNCT	27	punct
27	Oriol	PROPN	19	conj
28	Tranvia	PROPN	27	flat
29	-	PUNCT	19	punct
30	-	PUNCT	19	punct
31	,	PUNCT	32	punct
32	magos	NOUN	16	conj
33	-	PUNCT	35	punct
34	-	PUNCT	35	punct
35	Hausson	PROPN	32	appos
36	y	CCONJ	37	cc
37	Mag	PROPN	35	conj
38	Lari	PROPN	37	flat
39	-	PUNCT	35	punct
40	-	PUNCT	35	punct
41	y	CCONJ	42	cc
42	actores	NOUN	16	conj
43	como	SCONJ	44	mark
44	Pavlovsky	PROPN	42	nmod
45	,	PUNCT	46	punct
46	Vicky	PROPN	44	conj
47	Peña	PROPN	46	flat
48	,	PUNCT	49	punct
49	Carme	PROPN	44	conj
50	Sansa	PROPN	49	flat
51	,	PUNCT	52	punct
52	Ferran	PROPN	44	conj
53	Rañé	PROPN	52	flat
54	y	CCONJ	55	cc
55	Feliu	PROPN	44	conj
56	Formosa	PROPN	55	flat
57	,	PUNCT	59	punct
58	entre	ADP	59	case
59	otros	PRON	16	nmod
60	.	PUNCT	3	punct