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:
Felicia, la joven y desorientada irlandesa cuyo viaje da título a la película, llega a Birmingham en busca de su novio, alistado en el ejército, y cae en las extrañas, sugerentes y enigmáticas redes del personaje de Hoskins, un hombre maduro que se niega a crecer.
Postion	Tokenization
1	Felicia
2	,
3	la
4	joven
5	y
6	desorientada
7	irlandesa
8	cuyo
9	viaje
10	da
11	título
12	a
13	la
14	película
15	,
16	llega
17	a
18	Birmingham
19	en
20	busca
21	de
22	su
23	novio
24	,
25	alistado
26	en
27	el
28	ejército
29	,
30	y
31	cae
32	en
33	las
34	extrañas
35	,
36	sugerentes
37	y
38	enigmáticas
39	redes
40	del
41	personaje
42	de
43	Hoskins
44	,
45	un
46	hombre
47	maduro
48	que
49	se
50	niega
51	a
52	crecer
53	.
Position	Tokenization	UPOS	Head	Deprel
1	Felicia	PROPN	16	nsubj
2	,	PUNCT	7	punct
3	la	DET	7	det
4	joven	ADJ	7	amod
5	y	CCONJ	6	cc
6	desorientada	ADJ	4	conj
7	irlandesa	NOUN	1	appos
8	cuyo	PRON	9	nmod
9	viaje	NOUN	10	nsubj
10	da	VERB	7	acl
11	título	NOUN	10	obj
12	a	ADP	14	case
13	la	DET	14	det
14	película	NOUN	10	obj
15	,	PUNCT	7	punct
16	llega	VERB	0	root
17	a	ADP	18	case
18	Birmingham	PROPN	16	obl
19	en	ADP	23	case
20	busca	NOUN	19	fixed
21	de	ADP	19	fixed
22	su	DET	23	det
23	novio	NOUN	16	obl
24	,	PUNCT	25	punct
25	alistado	ADJ	23	amod
26	en	ADP	28	case
27	el	DET	28	det
28	ejército	NOUN	25	obl
29	,	PUNCT	25	punct
30	y	CCONJ	31	cc
31	cae	VERB	16	conj
32	en	ADP	39	case
33	las	DET	39	det
34	extrañas	ADJ	39	amod
35	,	PUNCT	36	punct
36	sugerentes	ADJ	34	conj
37	y	CCONJ	38	cc
38	enigmáticas	ADJ	34	conj
39	redes	NOUN	31	obl
40	del	ADP	41	case
41	personaje	NOUN	39	nmod
42	de	ADP	43	case
43	Hoskins	PROPN	41	nmod
44	,	PUNCT	46	punct
45	un	DET	46	det
46	hombre	NOUN	41	appos
47	maduro	ADJ	46	amod
48	que	PRON	50	nsubj
49	se	PRON	50	expl:pv
50	niega	VERB	46	acl
51	a	ADP	52	mark
52	crecer	VERB	50	xcomp
53	.	PUNCT	16	punct