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 text: I am now modelling the Queensland electricity spot price using Geometric Brownian Mean Reverting Jump Diffusion Model and have followed your paper "Making the most of mean reversion" to estimate the mean reversion speed.
Position	Tokenization	UPOS	Head	Deprel
1	I	PRON	4	nsubj
2	am	AUX	4	aux
3	now	ADV	4	advmod
4	modelling	VERB	0	root
5	the	DET	9	det
6	Queensland	PROPN	7	compound
7	electricity	NOUN	9	compound
8	spot	NOUN	9	compound
9	price	NOUN	4	obj
10	using	VERB	4	advcl
11	Geometric	ADJ	17	amod
12	Brownian	ADJ	17	amod
13	Mean	NOUN	14	compound
14	Reverting	VERB	17	amod
15	Jump	NOUN	16	compound
16	Diffusion	NOUN	17	compound
17	Model	NOUN	10	obj
18	and	CCONJ	20	cc
19	have	AUX	20	aux
20	followed	VERB	4	conj
21	your	PRON	22	nmod:poss
22	paper	NOUN	20	obj
23	"	PUNCT	24	punct
24	Making	VERB	22	appos
25	the	DET	26	det
26	most	ADJ	24	obj
27	of	ADP	29	case
28	mean	PROPN	29	compound
29	reversion	PROPN	24	nmod
30	"	PUNCT	24	punct
31	to	PART	32	mark
32	estimate	VERB	20	advcl
33	the	DET	36	det
34	mean	NOUN	36	compound
35	reversion	NOUN	36	compound
36	speed	NOUN	32	obj
37	.	PUNCT	4	punct