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 año pasado, 44.000 de los casi 400.000 abetos vendidos en Barcelona fueron recuperados y permitieron obtener 553 metros cúbicos de compostaje que fueron utilizados para mejorar el sustrato de más de 8.000 árboles y abonar nueve hectáreas de césped.
Postion	Tokenization
1	El
2	año
3	pasado
4	,
5	44.000
6	de
7	los
8	casi
9	400.000
10	abetos
11	vendidos
12	en
13	Barcelona
14	fueron
15	recuperados
16	y
17	permitieron
18	obtener
19	553
20	metros
21	cúbicos
22	de
23	compostaje
24	que
25	fueron
26	utilizados
27	para
28	mejorar
29	el
30	sustrato
31	de
32	más
33	de
34	8.000
35	árboles
36	y
37	abonar
38	nueve
39	hectáreas
40	de
41	césped
42	.
Position	Tokenization	UPOS	Head	Deprel
1	El	DET	2	det
2	año	NOUN	15	obl
3	pasado	ADJ	2	amod
4	,	PUNCT	2	punct
5	44.000	NUM	15	nsubj
6	de	ADP	10	case
7	los	DET	9	det
8	casi	ADV	9	advmod
9	400.000	NUM	10	nummod
10	abetos	NOUN	5	nmod
11	vendidos	ADJ	10	amod
12	en	ADP	13	case
13	Barcelona	PROPN	11	obl
14	fueron	AUX	15	aux
15	recuperados	VERB	0	root
16	y	CCONJ	17	cc
17	permitieron	VERB	15	conj
18	obtener	VERB	17	xcomp
19	553	NUM	20	nummod
20	metros	NOUN	18	obj
21	cúbicos	NOUN	20	compound
22	de	ADP	23	case
23	compostaje	NOUN	20	nmod
24	que	PRON	26	nsubj
25	fueron	AUX	26	aux
26	utilizados	VERB	20	acl
27	para	ADP	28	mark
28	mejorar	VERB	26	advcl
29	el	DET	30	det
30	sustrato	NOUN	28	obj
31	de	ADP	35	case
32	más	ADV	35	advmod
33	de	ADP	32	case
34	8.000	NUM	32	nummod
35	árboles	NOUN	30	nmod
36	y	CCONJ	37	cc
37	abonar	VERB	28	conj
38	nueve	NUM	39	nummod
39	hectáreas	NOUN	37	obj
40	de	ADP	41	case
41	césped	NOUN	39	nmod
42	.	PUNCT	15	punct