how to match a sentence to a cluster of keywords? I have a classification problem. I have clusters called 'Experience', 'Education', 'Abilities' . The labelled data (72,000+ entries with all clusters together) with two columns looks like below. 

year of education               Education
years education                 Education
years of educational            Education
two years of education          Education
years of education beyond       Education
education four year             Education
..........
of proven sales experience      Ability
knowledge of and                Ability
experience or education high    Ability
assigned knowledge skills       Ability
accountable for driving         Ability
..........
administrative and leadership skills    Experience
advanced negotiations skills            Experience
must have keyboarding skills            Experience
must have skills                        Experience
activities preferred skills             Experience
of clinical skill                       Experience


I have to give a string and find it whether it belongs to either Experience or Education or Ability based on the trained model. The examples of strings . 

string1 = "There is a requirement of four year professional degree"
string2 = "Able to drive the teams to higher levels"
string3 = "Must have programming experience in C, C++"


when I test these string it should be able to classify the string in to either of the clusters. 

What are the possible ways to train my model ? Refering to word2vec and doc2vec, will these models work? I couldn't find any relevant examples to train model on words and test on strings. Any ideas on how to work?
I am kind of confused by the question. But I am guessing you are asking what model to use and how to represent words. 

This seems like a pretty introductory example so you may want to try simple Bag-of-words representation. Try pushing that into a Naive Bayes classifier as a first pass. If you are unhappy with the results, you can implement word2vec and more complex methods.