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 la gala, que se celebró en uno de los salones del Hotel Plaza y sirvió para recaudar unos 600.000 dólares, asistieron, entre otros, Pablo de Grecia y su esposa Chantal; Herman Badillo, presidente de la Universidad pública de Nueva York CUNY; John Brademas, presidente honorario de la Universidad de Nueva York, el diseñador venezolano Oscar de la Renta y el ex embajador de EEUU en España Richard Gardner.
Postion	Tokenization
1	A
2	la
3	gala
4	,
5	que
6	se
7	celebró
8	en
9	uno
10	de
11	los
12	salones
13	del
14	Hotel
15	Plaza
16	y
17	sirvió
18	para
19	recaudar
20	unos
21	600.000
22	dólares
23	,
24	asistieron
25	,
26	entre
27	otros
28	,
29	Pablo
30	de
31	Grecia
32	y
33	su
34	esposa
35	Chantal
36	;
37	Herman
38	Badillo
39	,
40	presidente
41	de
42	la
43	Universidad
44	pública
45	de
46	Nueva
47	York
48	CUNY
49	;
50	John
51	Brademas
52	,
53	presidente
54	honorario
55	de
56	la
57	Universidad
58	de
59	Nueva
60	York
61	,
62	el
63	diseñador
64	venezolano
65	Oscar
66	de
67	la
68	Renta
69	y
70	el
71	ex
72	embajador
73	de
74	EEUU
75	en
76	España
77	Richard
78	Gardner
79	.
Position	Tokenization	UPOS	Head	Deprel
1	A	ADP	3	case
2	la	DET	3	det
3	gala	NOUN	24	obl
4	,	PUNCT	7	punct
5	que	PRON	7	nsubj
6	se	PRON	7	expl:pass
7	celebró	VERB	3	acl
8	en	ADP	9	case
9	uno	PRON	7	obl
10	de	ADP	12	case
11	los	DET	12	det
12	salones	NOUN	9	nmod
13	del	ADP	14	case
14	Hotel	PROPN	12	nmod
15	Plaza	PROPN	14	flat
16	y	CCONJ	17	cc
17	sirvió	VERB	7	conj
18	para	ADP	19	mark
19	recaudar	VERB	17	xcomp
20	unos	PRON	21	nmod
21	600.000	NUM	22	nummod
22	dólares	NOUN	19	obj
23	,	PUNCT	7	punct
24	asistieron	VERB	0	root
25	,	PUNCT	27	punct
26	entre	ADP	27	case
27	otros	PRON	29	nmod
28	,	PUNCT	27	punct
29	Pablo	PROPN	24	nsubj
30	de	ADP	31	case
31	Grecia	PROPN	29	flat
32	y	CCONJ	34	cc
33	su	DET	34	det
34	esposa	NOUN	29	conj
35	Chantal	PROPN	34	appos
36	;	PUNCT	37	punct
37	Herman	PROPN	29	flat
38	Badillo	PROPN	37	flat
39	,	PUNCT	40	punct
40	presidente	NOUN	37	appos
41	de	ADP	43	case
42	la	DET	43	det
43	Universidad	PROPN	40	nmod
44	pública	ADJ	43	amod
45	de	ADP	46	case
46	Nueva	PROPN	43	nmod
47	York	PROPN	46	flat
48	CUNY	PROPN	46	flat
49	;	PUNCT	50	punct
50	John	PROPN	29	flat
51	Brademas	PROPN	50	flat
52	,	PUNCT	53	punct
53	presidente	NOUN	50	appos
54	honorario	ADJ	53	amod
55	de	ADP	57	case
56	la	DET	57	det
57	Universidad	PROPN	53	nmod
58	de	ADP	59	case
59	Nueva	PROPN	57	flat
60	York	PROPN	59	flat
61	,	PUNCT	63	punct
62	el	DET	63	det
63	diseñador	NOUN	29	appos
64	venezolano	ADJ	63	amod
65	Oscar	PROPN	63	appos
66	de	ADP	68	case
67	la	DET	68	det
68	Renta	PROPN	65	flat
69	y	CCONJ	72	cc
70	el	DET	72	det
71	ex	ADJ	72	amod
72	embajador	NOUN	29	conj
73	de	ADP	74	case
74	EEUU	PROPN	72	nmod
75	en	ADP	76	case
76	España	PROPN	72	nmod
77	Richard	PROPN	72	appos
78	Gardner	PROPN	77	flat
79	.	PUNCT	24	punct