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:
Porque, como dice mi amiga la Susi, si la señora Aguirre quiere de veras castigar la suciedad de Madrid en general, y del día de huelga en particular, que multe al ministro Asunción por poner perdido un día tan hermoso con tantos piquetes policiales, o a todos los voceros que han contaminado el aire de enero ensuciando la hermosa palabra - normalidad -, o, ya puestos, que multe a Tabacalera con mil duros por cada colilla que recojan del suelo los barrenderos municipales.
Postion	Tokenization
1	Porque
2	,
3	como
4	dice
5	mi
6	amiga
7	la
8	Susi
9	,
10	si
11	la
12	señora
13	Aguirre
14	quiere
15	de
16	veras
17	castigar
18	la
19	suciedad
20	de
21	Madrid
22	en
23	general
24	,
25	y
26	del
27	día
28	de
29	huelga
30	en
31	particular
32	,
33	que
34	multe
35	al
36	ministro
37	Asunción
38	por
39	poner
40	perdido
41	un
42	día
43	tan
44	hermoso
45	con
46	tantos
47	piquetes
48	policiales
49	,
50	o
51	a
52	todos
53	los
54	voceros
55	que
56	han
57	contaminado
58	el
59	aire
60	de
61	enero
62	ensuciando
63	la
64	hermosa
65	palabra
66	-
67	normalidad
68	-
69	,
70	o
71	,
72	ya
73	puestos
74	,
75	que
76	multe
77	a
78	Tabacalera
79	con
80	mil
81	duros
82	por
83	cada
84	colilla
85	que
86	recojan
87	del
88	suelo
89	los
90	barrenderos
91	municipales
92	.
Position	Tokenization	UPOS	Head	Deprel
1	Porque	SCONJ	4	mark
2	,	PUNCT	4	punct
3	como	SCONJ	4	mark
4	dice	VERB	0	root
5	mi	DET	6	det
6	amiga	NOUN	4	nsubj
7	la	DET	8	det
8	Susi	PROPN	6	appos
9	,	PUNCT	4	punct
10	si	SCONJ	14	mark
11	la	DET	12	det
12	señora	NOUN	14	nsubj
13	Aguirre	PROPN	12	appos
14	quiere	VERB	4	advcl
15	de	ADP	14	advmod
16	veras	NOUN	15	fixed
17	castigar	VERB	14	xcomp
18	la	DET	19	det
19	suciedad	NOUN	17	obj
20	de	ADP	21	case
21	Madrid	PROPN	19	nmod
22	en	ADP	21	advmod
23	general	NOUN	22	fixed
24	,	PUNCT	27	punct
25	y	CCONJ	27	cc
26	del	ADP	27	case
27	día	NOUN	21	conj
28	de	ADP	29	case
29	huelga	NOUN	27	nmod
30	en	ADP	27	advmod
31	particular	NOUN	30	fixed
32	,	PUNCT	14	punct
33	que	SCONJ	34	mark
34	multe	VERB	4	advcl
35	al	ADP	36	case
36	ministro	NOUN	34	obj
37	Asunción	PROPN	36	appos
38	por	ADP	40	case
39	poner	VERB	34	advcl
40	perdido	ADJ	39	xcomp
41	un	DET	42	det
42	día	NOUN	40	obj
43	tan	ADV	44	advmod
44	hermoso	ADJ	42	amod
45	con	ADP	47	case
46	tantos	DET	47	det
47	piquetes	NOUN	39	obl
48	policiales	ADJ	47	amod
49	,	PUNCT	34	punct
50	o	CCONJ	76	cc
51	a	ADP	54	case
52	todos	DET	53	det
53	los	DET	54	det
54	voceros	NOUN	34	obl
55	que	PRON	57	nsubj
56	han	AUX	57	aux
57	contaminado	VERB	54	acl
58	el	DET	59	det
59	aire	NOUN	57	obj
60	de	ADP	61	case
61	enero	NOUN	59	nmod
62	ensuciando	VERB	57	advcl
63	la	DET	65	det
64	hermosa	ADJ	65	amod
65	palabra	NOUN	62	obj
66	-	PUNCT	67	punct
67	normalidad	NOUN	65	appos
68	-	PUNCT	67	punct
69	,	PUNCT	54	punct
70	o	CCONJ	76	cc
71	,	PUNCT	73	punct
72	ya	ADV	73	advmod
73	puestos	ADJ	76	advmod
74	,	PUNCT	73	punct
75	que	SCONJ	76	mark
76	multe	VERB	34	conj
77	a	ADP	78	case
78	Tabacalera	PROPN	76	obj
79	con	ADP	81	case
80	mil	NUM	81	nummod
81	duros	NOUN	76	obl
82	por	ADP	84	case
83	cada	DET	84	det
84	colilla	NOUN	76	obl
85	que	PRON	86	obj
86	recojan	VERB	84	acl
87	del	ADP	88	case
88	suelo	NOUN	86	obl
89	los	DET	90	det
90	barrenderos	NOUN	86	nsubj
91	municipales	ADJ	90	amod
92	.	PUNCT	4	punct