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 candidata al Congreso por el PP por la provincia de Las Palmas, María Bernarda Barrios, conocida en las islas como "Nardy", se ha montado su propio dispositivo electoral, que ella misma denomina el "Nardy-Móvil" y que consiste en un peculiar vehículo en el que se traslada a barrios, pueblos y pedanías para pedir el voto.
Postion	Tokenization
1	La
2	candidata
3	al
4	Congreso
5	por
6	el
7	PP
8	por
9	la
10	provincia
11	de
12	Las
13	Palmas
14	,
15	María
16	Bernarda
17	Barrios
18	,
19	conocida
20	en
21	las
22	islas
23	como
24	"
25	Nardy
26	"
27	,
28	se
29	ha
30	montado
31	su
32	propio
33	dispositivo
34	electoral
35	,
36	que
37	ella
38	misma
39	denomina
40	el
41	"
42	Nardy-Móvil
43	"
44	y
45	que
46	consiste
47	en
48	un
49	peculiar
50	vehículo
51	en
52	el
53	que
54	se
55	traslada
56	a
57	barrios
58	,
59	pueblos
60	y
61	pedanías
62	para
63	pedir
64	el
65	voto
66	.
Position	Tokenization	UPOS	Head	Deprel
1	La	DET	2	det
2	candidata	NOUN	30	nsubj
3	al	ADP	4	case
4	Congreso	PROPN	2	nmod
5	por	ADP	7	case
6	el	DET	7	det
7	PP	PROPN	2	nmod
8	por	ADP	10	case
9	la	DET	10	det
10	provincia	NOUN	2	nmod
11	de	ADP	13	case
12	Las	DET	13	det
13	Palmas	PROPN	10	nmod
14	,	PUNCT	15	punct
15	María	PROPN	2	appos
16	Bernarda	PROPN	15	flat
17	Barrios	PROPN	15	flat
18	,	PUNCT	15	punct
19	conocida	ADJ	2	amod
20	en	ADP	22	case
21	las	DET	22	det
22	islas	NOUN	19	obl
23	como	SCONJ	25	mark
24	"	PUNCT	25	punct
25	Nardy	PROPN	19	obj
26	"	PUNCT	25	punct
27	,	PUNCT	2	punct
28	se	PRON	30	iobj
29	ha	AUX	30	aux
30	montado	VERB	0	root
31	su	DET	33	det
32	propio	DET	31	det
33	dispositivo	NOUN	30	obj
34	electoral	ADJ	33	amod
35	,	PUNCT	39	punct
36	que	PRON	39	obj
37	ella	PRON	39	nsubj
38	misma	DET	37	det
39	denomina	VERB	33	acl
40	el	DET	42	det
41	"	PUNCT	42	punct
42	Nardy-Móvil	PROPN	39	obj
43	"	PUNCT	42	punct
44	y	CCONJ	46	cc
45	que	PRON	46	nsubj
46	consiste	VERB	39	conj
47	en	ADP	50	case
48	un	DET	50	det
49	peculiar	ADJ	50	amod
50	vehículo	NOUN	46	obj
51	en	ADP	53	case
52	el	DET	53	det
53	que	PRON	55	obl
54	se	PRON	55	expl:pv
55	traslada	VERB	50	acl
56	a	ADP	57	case
57	barrios	NOUN	55	obl
58	,	PUNCT	59	punct
59	pueblos	NOUN	57	conj
60	y	CCONJ	61	cc
61	pedanías	NOUN	57	conj
62	para	ADP	63	mark
63	pedir	VERB	55	advcl
64	el	DET	65	det
65	voto	NOUN	63	obj
66	.	PUNCT	30	punct