Semantic networks: word2vec? I have some doubts on how to represent the relationships between words in texts.
Let’s suppose I have two sentences like these:
Angela Merkel is a German politician who has been Chancellor of Germany since 2005.

What I would expect is a connection between name Angela and Merkel (Angela is the name, Merkel the surname) who is German and politician and Chancellor.
I read about the use of word2vec to determine the semantic structure of a sentence.
My question is therefore if this model can allow me to determine such semantic structure or if another method would be better.
There are a few models that are trained to analyse a sentence and classify each token (or recognise dependencies between words).

Part of speech tagging (POS) models assign to each word its function (noun, verb, ...) - have a look at this link

Dependency parsing (DP) models will recognize which words go together (in this case Angela and Merkel for instance) - check this out

Named entity recognition (NER) models will for instance say that &quot;Angela Merkel&quot; is a person, &quot;Germany&quot; is a country ... - another link