Similarity in Spacy
I am trying to understand how similarity in Spacy works. I tried using Melania Trump's speech and Michelle Obama's speech to see how similar they were.
This is my code.
import spacy
nlp = spacy.load('en_core_web_lg')
file1 = open("melania.txt").read().decode('ascii', 'ignore')
file2 = open("michelle.txt").read().decode('ascii', 'ignore')
doc1 = nlp(unicode(file1))
doc2 = nlp(unicode(file2))
print doc1.similarity(doc2)
I get the similarity score as 0.9951584208511974. This similarity score looks very high to me. Is this correct? Am I doing something wrong?
nlp similarity spacy
asked Nov 23 '18 at 22:35
thehydrogenthehydrogen
35
add a comment |
I am trying to understand how similarity in Spacy works. I tried using Melania Trump's speech and Michelle Obama's speech to see how similar they were.
This is my code.
import spacy
nlp = spacy.load('en_core_web_lg')
file1 = open("melania.txt").read().decode('ascii', 'ignore')
file2 = open("michelle.txt").read().decode('ascii', 'ignore')
doc1 = nlp(unicode(file1))
doc2 = nlp(unicode(file2))
print doc1.similarity(doc2)
I get the similarity score as 0.9951584208511974. This similarity score looks very high to me. Is this correct? Am I doing something wrong?
nlp similarity spacy
asked Nov 23 '18 at 22:35
thehydrogenthehydrogen
35
add a comment |
I am trying to understand how similarity in Spacy works. I tried using Melania Trump's speech and Michelle Obama's speech to see how similar they were.
This is my code.
import spacy
nlp = spacy.load('en_core_web_lg')
file1 = open("melania.txt").read().decode('ascii', 'ignore')
file2 = open("michelle.txt").read().decode('ascii', 'ignore')
doc1 = nlp(unicode(file1))
doc2 = nlp(unicode(file2))
print doc1.similarity(doc2)
I get the similarity score as 0.9951584208511974. This similarity score looks very high to me. Is this correct? Am I doing something wrong?
nlp similarity spacy
asked Nov 23 '18 at 22:35
thehydrogenthehydrogen
35
I am trying to understand how similarity in Spacy works. I tried using Melania Trump's speech and Michelle Obama's speech to see how similar they were.
This is my code.
import spacy
nlp = spacy.load('en_core_web_lg')
file1 = open("melania.txt").read().decode('ascii', 'ignore')
file2 = open("michelle.txt").read().decode('ascii', 'ignore')
doc1 = nlp(unicode(file1))
doc2 = nlp(unicode(file2))
print doc1.similarity(doc2)
I get the similarity score as 0.9951584208511974. This similarity score looks very high to me. Is this correct? Am I doing something wrong?
nlp similarity spacy
nlp similarity spacy
asked Nov 23 '18 at 22:35
thehydrogenthehydrogen
35
asked Nov 23 '18 at 22:35
thehydrogenthehydrogen
35
asked Nov 23 '18 at 22:35
thehydrogenthehydrogen
35
asked Nov 23 '18 at 22:35
thehydrogenthehydrogen
35
asked Nov 23 '18 at 22:35
thehydrogenthehydrogen
35
35
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
By default spaCy calculates cosine similarity. Similarity is determined by comparing word vectors or word embeddings, multi-dimensional meaning representations of a word.
It returns return (numpy.dot(self.vector, other.vector) / (self_norm * other_norm))
text1 = 'How can I end violence?'
text2 = 'What should I do to be a peaceful?'
doc1 = nlp(text1)
doc2 = nlp(text2)
print("spaCy :", doc1.similarity(doc2))
print(np.dot(doc1.vector, doc2.vector) / (np.linalg.norm(doc1.vector) * np.linalg.norm(doc2.vector)))
Output:
spaCy : 0.916553147896471
0.9165532
It seems that spaCy's .vector method created the vectors. Documentation says that spaCy's models are trained from GloVe's vectors.
answered Nov 24 '18 at 8:47
Srce CdeSrce Cde
1,134511
thank you. I wanted to know why the score is so high. any insights?
– thehydrogen
Nov 24 '18 at 16:32
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
By default spaCy calculates cosine similarity. Similarity is determined by comparing word vectors or word embeddings, multi-dimensional meaning representations of a word.
It returns return (numpy.dot(self.vector, other.vector) / (self_norm * other_norm))
text1 = 'How can I end violence?'
text2 = 'What should I do to be a peaceful?'
doc1 = nlp(text1)
doc2 = nlp(text2)
print("spaCy :", doc1.similarity(doc2))
print(np.dot(doc1.vector, doc2.vector) / (np.linalg.norm(doc1.vector) * np.linalg.norm(doc2.vector)))
Output:
spaCy : 0.916553147896471
0.9165532
It seems that spaCy's .vector method created the vectors. Documentation says that spaCy's models are trained from GloVe's vectors.
answered Nov 24 '18 at 8:47
Srce CdeSrce Cde
1,134511
thank you. I wanted to know why the score is so high. any insights?
– thehydrogen
Nov 24 '18 at 16:32
add a comment |
By default spaCy calculates cosine similarity. Similarity is determined by comparing word vectors or word embeddings, multi-dimensional meaning representations of a word.
It returns return (numpy.dot(self.vector, other.vector) / (self_norm * other_norm))
text1 = 'How can I end violence?'
text2 = 'What should I do to be a peaceful?'
doc1 = nlp(text1)
doc2 = nlp(text2)
print("spaCy :", doc1.similarity(doc2))
print(np.dot(doc1.vector, doc2.vector) / (np.linalg.norm(doc1.vector) * np.linalg.norm(doc2.vector)))
Output:
spaCy : 0.916553147896471
0.9165532
It seems that spaCy's .vector method created the vectors. Documentation says that spaCy's models are trained from GloVe's vectors.
answered Nov 24 '18 at 8:47
Srce CdeSrce Cde
1,134511
thank you. I wanted to know why the score is so high. any insights?
– thehydrogen
Nov 24 '18 at 16:32
add a comment |
By default spaCy calculates cosine similarity. Similarity is determined by comparing word vectors or word embeddings, multi-dimensional meaning representations of a word.
It returns return (numpy.dot(self.vector, other.vector) / (self_norm * other_norm))
text1 = 'How can I end violence?'
text2 = 'What should I do to be a peaceful?'
doc1 = nlp(text1)
doc2 = nlp(text2)
print("spaCy :", doc1.similarity(doc2))
print(np.dot(doc1.vector, doc2.vector) / (np.linalg.norm(doc1.vector) * np.linalg.norm(doc2.vector)))
Output:
spaCy : 0.916553147896471
0.9165532
It seems that spaCy's .vector method created the vectors. Documentation says that spaCy's models are trained from GloVe's vectors.
answered Nov 24 '18 at 8:47
Srce CdeSrce Cde
1,134511
By default spaCy calculates cosine similarity. Similarity is determined by comparing word vectors or word embeddings, multi-dimensional meaning representations of a word.
It returns return (numpy.dot(self.vector, other.vector) / (self_norm * other_norm))
text1 = 'How can I end violence?'
text2 = 'What should I do to be a peaceful?'
doc1 = nlp(text1)
doc2 = nlp(text2)
print("spaCy :", doc1.similarity(doc2))
print(np.dot(doc1.vector, doc2.vector) / (np.linalg.norm(doc1.vector) * np.linalg.norm(doc2.vector)))
Output:
spaCy : 0.916553147896471
0.9165532
It seems that spaCy's .vector method created the vectors. Documentation says that spaCy's models are trained from GloVe's vectors.
answered Nov 24 '18 at 8:47
Srce CdeSrce Cde
1,134511
answered Nov 24 '18 at 8:47
Srce CdeSrce Cde
1,134511
answered Nov 24 '18 at 8:47
Srce CdeSrce Cde
1,134511
answered Nov 24 '18 at 8:47
Srce CdeSrce Cde
1,134511
1,134511
thank you. I wanted to know why the score is so high. any insights?
– thehydrogen
Nov 24 '18 at 16:32
add a comment |
thank you. I wanted to know why the score is so high. any insights?
– thehydrogen
Nov 24 '18 at 16:32
thank you. I wanted to know why the score is so high. any insights?
– thehydrogen
Nov 24 '18 at 16:32
thank you. I wanted to know why the score is so high. any insights?
– thehydrogen
Nov 24 '18 at 16:32
add a comment |
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