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 population se concentre principalement sur l'arrondissement de Perpignan puisqu'on y recense en 2007 74% de la population totale du département, avec une densité de 246 habitants/km², contre 16% pour l'arrondissement de Céret et 10% pour celui de Prades.
Postion	Tokenization
1	La
2	population
3	se
4	concentre
5	principalement
6	sur
7	l'
8	arrondissement
9	de
10	Perpignan
11	puisqu'
12	on
13	y
14	recense
15	en
16	2007
17	74
18	%
19	de
20	la
21	population
22	totale
23	du
24	de
25	le
26	département
27	,
28	avec
29	une
30	densité
31	de
32	246
33	habitants
34	/
35	km²
36	,
37	contre
38	16
39	%
40	pour
41	l'
42	arrondissement
43	de
44	Céret
45	et
46	10
47	%
48	pour
49	celui
50	de
51	Prades
52	.
Position	Tokenization	UPOS	Head	Deprel
1	La	DET	2	det
2	population	NOUN	4	nsubj:pass
3	se	PRON	4	expl:pass
4	concentre	VERB	0	root
5	principalement	ADV	4	advmod
6	sur	ADP	8	case
7	l'	DET	8	det
8	arrondissement	NOUN	4	obl:mod
9	de	ADP	10	case
10	Perpignan	PROPN	8	nmod
11	puisqu'	SCONJ	14	mark
12	on	PRON	14	nsubj
13	y	PRON	14	obl:mod
14	recense	VERB	4	advcl
15	en	ADP	16	case
16	2007	NUM	14	obl:mod
17	74	NUM	18	nummod
18	%	SYM	14	obj
19	de	ADP	21	case
20	la	DET	21	det
21	population	NOUN	18	nmod
22	totale	ADJ	21	amod
23	du	_	None	_
24	de	ADP	25	case
25	le	DET	25	det
26	département	NOUN	21	nmod
27	,	PUNCT	29	punct
28	avec	ADP	29	case
29	une	DET	29	det
30	densité	NOUN	4	obl:mod
31	de	ADP	32	case
32	246	NUM	32	nummod
33	habitants	NOUN	29	nmod
34	/	SYM	34	cc
35	km²	NOUN	32	conj
36	,	PUNCT	38	punct
37	contre	ADP	38	case
38	16	NUM	38	nummod
39	%	SYM	4	obl:mod
40	pour	ADP	41	case
41	l'	DET	41	det
42	arrondissement	NOUN	38	nmod
43	de	ADP	43	case
44	Céret	PROPN	41	nmod
45	et	CCONJ	46	cc
46	10	NUM	46	nummod
47	%	SYM	38	conj
48	pour	ADP	48	case
49	celui	PRON	46	nmod
50	de	ADP	50	case
51	Prades	PROPN	48	nmod
52	.	PUNCT	4	punct