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:
Lothar Vosseler dejó de ser el trabajador anónimo cuando, a mediados de octubre, el rotativo BZ desveló que es hermano por parte de madre del canciller, que estuvo en el paro varios años y que gana 250.000 pesetas brutas al mes por limpiar tuberías.
Postion	Tokenization
1	Lothar
2	Vosseler
3	dejó
4	de
5	ser
6	el
7	trabajador
8	anónimo
9	cuando
10	,
11	a
12	mediados
13	de
14	octubre
15	,
16	el
17	rotativo
18	BZ
19	desveló
20	que
21	es
22	hermano
23	por
24	parte
25	de
26	madre
27	del
28	canciller
29	,
30	que
31	estuvo
32	en
33	el
34	paro
35	varios
36	años
37	y
38	que
39	gana
40	250.000
41	pesetas
42	brutas
43	al
44	mes
45	por
46	limpiar
47	tuberías
48	.
Position	Tokenization	UPOS	Head	Deprel
1	Lothar	PROPN	3	nsubj
2	Vosseler	PROPN	1	flat
3	dejó	VERB	0	root
4	de	ADP	7	case
5	ser	AUX	7	cop
6	el	DET	7	det
7	trabajador	NOUN	3	xcomp
8	anónimo	ADJ	7	amod
9	cuando	SCONJ	19	mark
10	,	PUNCT	14	punct
11	a	ADP	14	case
12	mediados	NOUN	11	fixed
13	de	ADP	11	fixed
14	octubre	NOUN	19	obl
15	,	PUNCT	14	punct
16	el	DET	17	det
17	rotativo	NOUN	19	nsubj
18	BZ	PROPN	17	appos
19	desveló	VERB	7	advcl
20	que	SCONJ	22	mark
21	es	AUX	22	cop
22	hermano	NOUN	19	ccomp
23	por	ADP	26	case
24	parte	NOUN	23	fixed
25	de	ADP	23	fixed
26	madre	NOUN	22	nmod
27	del	ADP	28	case
28	canciller	NOUN	22	nmod
29	,	PUNCT	34	punct
30	que	SCONJ	34	mark
31	estuvo	AUX	34	cop
32	en	ADP	34	case
33	el	DET	34	det
34	paro	NOUN	22	advcl
35	varios	DET	36	det
36	años	NOUN	34	obl
37	y	CCONJ	39	cc
38	que	SCONJ	39	mark
39	gana	VERB	22	conj
40	250.000	NUM	41	nummod
41	pesetas	NOUN	39	obj
42	brutas	ADJ	41	amod
43	al	ADP	44	case
44	mes	NOUN	41	nmod
45	por	ADP	46	mark
46	limpiar	VERB	39	advcl
47	tuberías	NOUN	46	obj
48	.	PUNCT	3	punct