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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Short-Sentence Similarity using Gensim Word Mover Distance
1. Gensim Word-Movers model
Reference
Step1:  Load the Google's pre-trained model
Step2:  Algorithm
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Python Code:
# Importing the dependecies
import gensim
Explanation: Short-Sentence Similarity using Gensim Word Mover Distance
1. Gensim Word-Movers model
Reference:
Note: Refer to other similarity functions
https://radimrehurek.com/gensim/models/word2vec.html
End of explanation
#load word2vec model, here GoogleNews is used , this should be downloaded and to be loaded from the local path
model = gensim.models.KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary=True)
# intent list for the application
intent_list = ['Read the news', 'Hello', 'Get my news', 'Get feed', 'Read my feed']
Explanation: Load the Google's pre-trained model
End of explanation
print ("provide your intent")
input_intent = input()
intent_similiarity_map = dict()
print ("\n")
for each in intent_list:
    
    #calculate distance between two sentences using WMD algorithm (Word Movers Distance)
    distance = model.wmdistance(each, input_intent)
    # map the values into a dictionary
    intent_similiarity_map[each] = distance
    
print (intent_similiarity_map)
print ("\n")
print ("Selected Intent for the given user input")
# pick the intent with minimum distance
print (min(intent_similiarity_map, key = intent_similiarity_map.get))    
Explanation: Algorithm
End of explanation
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