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:
Caja Rural de Asturias prevé obtener este ejercicio unos beneficios netos de 2.237 millones de pesetas, lo que supone un incremento del 5 por ciento respecto a 1999, con lo que volverá a batir su "récord" de rentabilidad, según indicó el presidente de la entidad, Román Suárez Blanco.
Postion	Tokenization
1	Caja
2	Rural
3	de
4	Asturias
5	prevé
6	obtener
7	este
8	ejercicio
9	unos
10	beneficios
11	netos
12	de
13	2.237
14	millones
15	de
16	pesetas
17	,
18	lo
19	que
20	supone
21	un
22	incremento
23	del
24	5
25	por
26	ciento
27	respecto
28	a
29	1999
30	,
31	con
32	lo
33	que
34	volverá
35	a
36	batir
37	su
38	"
39	récord
40	"
41	de
42	rentabilidad
43	,
44	según
45	indicó
46	el
47	presidente
48	de
49	la
50	entidad
51	,
52	Román
53	Suárez
54	Blanco
55	.
Position	Tokenization	UPOS	Head	Deprel
1	Caja	PROPN	5	nsubj
2	Rural	PROPN	1	flat
3	de	ADP	4	case
4	Asturias	PROPN	1	flat
5	prevé	VERB	0	root
6	obtener	VERB	5	xcomp
7	este	DET	8	det
8	ejercicio	NOUN	6	obl
9	unos	DET	10	det
10	beneficios	NOUN	6	obj
11	netos	ADJ	10	amod
12	de	ADP	14	case
13	2.237	NUM	14	nummod
14	millones	NOUN	10	nmod
15	de	ADP	16	case
16	pesetas	NOUN	14	nmod
17	,	PUNCT	20	punct
18	lo	PRON	20	det
19	que	PRON	20	nsubj
20	supone	VERB	5	advcl
21	un	DET	22	det
22	incremento	NOUN	20	obj
23	del	ADP	24	case
24	5	NUM	22	nummod
25	por	ADP	26	case
26	ciento	NUM	24	compound
27	respecto	NOUN	29	case
28	a	ADP	27	fixed
29	1999	NOUN	22	nmod
30	,	PUNCT	34	punct
31	con	ADP	33	case
32	lo	PRON	33	det
33	que	PRON	34	obl
34	volverá	VERB	22	acl
35	a	ADP	36	mark
36	batir	VERB	34	xcomp
37	su	DET	39	det
38	"	PUNCT	39	punct
39	récord	NOUN	34	obj
40	"	PUNCT	39	punct
41	de	ADP	42	case
42	rentabilidad	NOUN	39	nmod
43	,	PUNCT	45	punct
44	según	SCONJ	45	mark
45	indicó	VERB	36	advcl
46	el	DET	47	det
47	presidente	NOUN	45	nsubj
48	de	ADP	50	case
49	la	DET	50	det
50	entidad	NOUN	47	nmod
51	,	PUNCT	52	punct
52	Román	PROPN	47	appos
53	Suárez	PROPN	52	flat
54	Blanco	PROPN	52	flat
55	.	PUNCT	5	punct