def find_entities(self, user_message):
tagged_user_message = pos_tag(preprocess(user_message))
message_nouns = extract_nouns(tagged_user_message)
tokens = word2vec(" ".join(message_nouns))
category = word2vec(blank_spot)
word2vec_result = compute_similarity(tokens, category)
word2vec_result.sort(key=lambda x: x[2])
return word2vec_result[-1][0]
def find_entities(self, user_message):
tagged_user_message = pos_tag(preprocess(user_message))
message_nouns = extract_nouns(tagged_user_message)
# fit a word2vec model on our candidate entities
tokens = word2vec(" ".join(message_nouns))
category = word2vec(blank_spot)
word2vec_result = compute_similarity(tokens, category)
# select the entity with the highest similarity score
word2vec_result.sort(key=lambda x: x[2])
if len(word2vec_result) > 0:
return word2vec_result[-1][0]
else:
return blank_spot
from user_functions import preprocess, compare_overlap, pos_tag, extract_nouns, word2vec, compute_similarity
from collections import Counter
user_message = "Good morning... will it rain in Chicago later this week?"
blank_spot = "illinois city"
response_a = "The average temperature this weekend in {} with be 88 degrees. Bring your sunglasses!"
response_b = "Forget about your umbrella; there is no rain forecasted in {} this weekend."
response_c = "This weekend, a warm front from the southeast will keep skies near {} clear."
responses = [response_a, response_b, response_c]
#preprocess documents
bow_user_message = Counter(preprocess(user_message))
processed_responses = [Counter(preprocess(response)) for response in responses]
#determine intent with BoW
similarity_list = [
compare_overlap(doc, bow_user_message) for doc in processed_responses
]
#extract entities with word2vec
tagged_user_message = pos_tag(preprocess(user_message))
message_nouns = extract_nouns(tagged_user_message)
#execute word2vec below
tokens = word2vec(" ".join(message_nouns))
category = word2vec(blank_spot)
word2vec_result = compute_similarity(tokens, category)
#select final response below