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:
La señora Aguirre, que tiene para la limpieza una mentalidad de bruja menopáusica, ha decidido multar a los sindicatos con 5.000 pesetas por cada octavilla tirada en las calles semivacías y semiapacibles del Madrid en huelga.
Postion	Tokenization
1	La
2	señora
3	Aguirre
4	,
5	que
6	tiene
7	para
8	la
9	limpieza
10	una
11	mentalidad
12	de
13	bruja
14	menopáusica
15	,
16	ha
17	decidido
18	multar
19	a
20	los
21	sindicatos
22	con
23	5.000
24	pesetas
25	por
26	cada
27	octavilla
28	tirada
29	en
30	las
31	calles
32	semivacías
33	y
34	semiapacibles
35	del
36	Madrid
37	en
38	huelga
39	.
Position	Tokenization	UPOS	Head	Deprel
1	La	DET	2	det
2	señora	NOUN	17	nsubj
3	Aguirre	PROPN	2	appos
4	,	PUNCT	6	punct
5	que	PRON	6	nsubj
6	tiene	VERB	2	acl
7	para	ADP	9	case
8	la	DET	9	det
9	limpieza	NOUN	6	obl
10	una	DET	11	det
11	mentalidad	NOUN	6	obj
12	de	ADP	13	case
13	bruja	NOUN	11	nmod
14	menopáusica	ADJ	13	amod
15	,	PUNCT	6	punct
16	ha	AUX	17	aux
17	decidido	VERB	0	root
18	multar	VERB	17	xcomp
19	a	ADP	21	case
20	los	DET	21	det
21	sindicatos	NOUN	18	obj
22	con	ADP	24	case
23	5.000	NUM	24	nummod
24	pesetas	NOUN	18	obl
25	por	ADP	27	case
26	cada	DET	27	det
27	octavilla	NOUN	24	nmod
28	tirada	ADJ	27	amod
29	en	ADP	31	case
30	las	DET	31	det
31	calles	NOUN	28	obl
32	semivacías	ADJ	31	amod
33	y	CCONJ	34	cc
34	semiapacibles	ADJ	32	conj
35	del	ADP	36	case
36	Madrid	PROPN	31	nmod
37	en	ADP	38	case
38	huelga	NOUN	36	nmod
39	.	PUNCT	17	punct