# Classify the main semantic relation of a sentence using keras

I tried to ask in SO, but they told me to ask here.

I have a big dataset like this:

RELATION<tab>SENTENCE
color<tab>The cat is black
color<tab>My dog is white
place<tab>Des Moines is in Iowa
place<tab>Des Moines is the capital of Iowa
is-a<tab>Des Moines is a city
is-a<tab>3D printer is a type of printer
is-a<tab>New Beetle was a car by Volkswagen
...


I need to build a classifier that, given a sentence, returns a relation as accurately as possible.

I have already programmed something with keras (python), but in this case I really don't know where to start from. For the moment I only realized that an important feature could be the order of the words in the sentence, but I don't know how to explot this.

Do you have some hint? E.g. about features, embeddings, hidden layers. May LSTM be a good NN? Why?

I hope this is not too broad, but I just need some hint.