How to get phrase count in Spacy phrasematcher
1
I am trying spaCy's PhraseMatcher. I have used an adaptation of the example given in the website like below.
color_patterns = [nlp(text) for text in ('red', 'green', 'yellow')]
product_patterns = [nlp(text) for text in ('boots', 'coats', 'bag')]
material_patterns = [nlp(text) for text in ('bat', 'yellow ball')]
matcher = PhraseMatcher(nlp.vocab)
matcher.add('COLOR', None, *color_patterns)
matcher.add('PRODUCT', None, *product_patterns)
matcher.add('MATERIAL', None, *material_patterns)
doc = nlp("yellow ball yellow lines")
matches = matcher(doc)
for match_id, start, end in matches:
rule_id = nlp.vocab.strings[match_id] # get the unicode ID, i.e. 'COLOR'
span = doc[start : end] # get the matched slice of the doc
print(rule_id, span.text)
The output is
COLOR yellow
MATERIAL ball
My question is how do I get the count of phrases such that my output looks like indicating yellow occurred twice and ball only once.
COLOR Yellow (2)
MATERIAL ball (1)
python-3.x nlp spacy
asked Nov 24 '18 at 19:43
venkatttaknevvenkatttaknev
498
add a comment |
1
I am trying spaCy's PhraseMatcher. I have used an adaptation of the example given in the website like below.
color_patterns = [nlp(text) for text in ('red', 'green', 'yellow')]
product_patterns = [nlp(text) for text in ('boots', 'coats', 'bag')]
material_patterns = [nlp(text) for text in ('bat', 'yellow ball')]
matcher = PhraseMatcher(nlp.vocab)
matcher.add('COLOR', None, *color_patterns)
matcher.add('PRODUCT', None, *product_patterns)
matcher.add('MATERIAL', None, *material_patterns)
doc = nlp("yellow ball yellow lines")
matches = matcher(doc)
for match_id, start, end in matches:
rule_id = nlp.vocab.strings[match_id] # get the unicode ID, i.e. 'COLOR'
span = doc[start : end] # get the matched slice of the doc
print(rule_id, span.text)
The output is
COLOR yellow
MATERIAL ball
My question is how do I get the count of phrases such that my output looks like indicating yellow occurred twice and ball only once.
COLOR Yellow (2)
MATERIAL ball (1)
python-3.x nlp spacy
asked Nov 24 '18 at 19:43
venkatttaknevvenkatttaknev
498
add a comment |
1
1
1
I am trying spaCy's PhraseMatcher. I have used an adaptation of the example given in the website like below.
color_patterns = [nlp(text) for text in ('red', 'green', 'yellow')]
product_patterns = [nlp(text) for text in ('boots', 'coats', 'bag')]
material_patterns = [nlp(text) for text in ('bat', 'yellow ball')]
matcher = PhraseMatcher(nlp.vocab)
matcher.add('COLOR', None, *color_patterns)
matcher.add('PRODUCT', None, *product_patterns)
matcher.add('MATERIAL', None, *material_patterns)
doc = nlp("yellow ball yellow lines")
matches = matcher(doc)
for match_id, start, end in matches:
rule_id = nlp.vocab.strings[match_id] # get the unicode ID, i.e. 'COLOR'
span = doc[start : end] # get the matched slice of the doc
print(rule_id, span.text)
The output is
COLOR yellow
MATERIAL ball
My question is how do I get the count of phrases such that my output looks like indicating yellow occurred twice and ball only once.
COLOR Yellow (2)
MATERIAL ball (1)
python-3.x nlp spacy
asked Nov 24 '18 at 19:43
venkatttaknevvenkatttaknev
498
I am trying spaCy's PhraseMatcher. I have used an adaptation of the example given in the website like below.
color_patterns = [nlp(text) for text in ('red', 'green', 'yellow')]
product_patterns = [nlp(text) for text in ('boots', 'coats', 'bag')]
material_patterns = [nlp(text) for text in ('bat', 'yellow ball')]
matcher = PhraseMatcher(nlp.vocab)
matcher.add('COLOR', None, *color_patterns)
matcher.add('PRODUCT', None, *product_patterns)
matcher.add('MATERIAL', None, *material_patterns)
doc = nlp("yellow ball yellow lines")
matches = matcher(doc)
for match_id, start, end in matches:
rule_id = nlp.vocab.strings[match_id] # get the unicode ID, i.e. 'COLOR'
span = doc[start : end] # get the matched slice of the doc
print(rule_id, span.text)
The output is
COLOR yellow
MATERIAL ball
My question is how do I get the count of phrases such that my output looks like indicating yellow occurred twice and ball only once.
COLOR Yellow (2)
MATERIAL ball (1)
python-3.x nlp spacy
python-3.x nlp spacy
asked Nov 24 '18 at 19:43
venkatttaknevvenkatttaknev
498
asked Nov 24 '18 at 19:43
venkatttaknevvenkatttaknev
498
asked Nov 24 '18 at 19:43
venkatttaknevvenkatttaknev
498
asked Nov 24 '18 at 19:43
venkatttaknevvenkatttaknev
498
asked Nov 24 '18 at 19:43
venkatttaknevvenkatttaknev
498
498
add a comment |
add a comment |
1 Answer
1
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oldest
votes
1
Something like this?
from collections import Counter
from spacy.matcher import PhraseMatcher
color_patterns = [nlp(text) for text in ('red', 'green', 'yellow')]
product_patterns = [nlp(text) for text in ('boots', 'coats', 'bag')]
material_patterns = [nlp(text) for text in ('bat', 'yellow ball')]
matcher = PhraseMatcher(nlp.vocab)
matcher.add('COLOR', None, *color_patterns)
matcher.add('PRODUCT', None, *product_patterns)
matcher.add('MATERIAL', None, *material_patterns)
d =
doc = nlp("yellow ball yellow lines")
matches = matcher(doc)
for match_id, start, end in matches:
rule_id = nlp.vocab.strings[match_id] # get the unicode ID, i.e. 'COLOR'
span = doc[start : end] # get the matched slice of the doc
d.append((rule_id, span.text))
print("n".join(f'{i[0]} {i[1]} ({j})' for i,j in Counter(d).items()))
Output:
COLOR yellow (2)
MATERIAL yellow ball (1)
answered Nov 25 '18 at 7:12
Srce CdeSrce Cde
1,144511
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
1
Something like this?
from collections import Counter
from spacy.matcher import PhraseMatcher
color_patterns = [nlp(text) for text in ('red', 'green', 'yellow')]
product_patterns = [nlp(text) for text in ('boots', 'coats', 'bag')]
material_patterns = [nlp(text) for text in ('bat', 'yellow ball')]
matcher = PhraseMatcher(nlp.vocab)
matcher.add('COLOR', None, *color_patterns)
matcher.add('PRODUCT', None, *product_patterns)
matcher.add('MATERIAL', None, *material_patterns)
d =
doc = nlp("yellow ball yellow lines")
matches = matcher(doc)
for match_id, start, end in matches:
rule_id = nlp.vocab.strings[match_id] # get the unicode ID, i.e. 'COLOR'
span = doc[start : end] # get the matched slice of the doc
d.append((rule_id, span.text))
print("n".join(f'{i[0]} {i[1]} ({j})' for i,j in Counter(d).items()))
Output:
COLOR yellow (2)
MATERIAL yellow ball (1)
answered Nov 25 '18 at 7:12
Srce CdeSrce Cde
1,144511
add a comment |
1
Something like this?
from collections import Counter
from spacy.matcher import PhraseMatcher
color_patterns = [nlp(text) for text in ('red', 'green', 'yellow')]
product_patterns = [nlp(text) for text in ('boots', 'coats', 'bag')]
material_patterns = [nlp(text) for text in ('bat', 'yellow ball')]
matcher = PhraseMatcher(nlp.vocab)
matcher.add('COLOR', None, *color_patterns)
matcher.add('PRODUCT', None, *product_patterns)
matcher.add('MATERIAL', None, *material_patterns)
d =
doc = nlp("yellow ball yellow lines")
matches = matcher(doc)
for match_id, start, end in matches:
rule_id = nlp.vocab.strings[match_id] # get the unicode ID, i.e. 'COLOR'
span = doc[start : end] # get the matched slice of the doc
d.append((rule_id, span.text))
print("n".join(f'{i[0]} {i[1]} ({j})' for i,j in Counter(d).items()))
Output:
COLOR yellow (2)
MATERIAL yellow ball (1)
answered Nov 25 '18 at 7:12
Srce CdeSrce Cde
1,144511
add a comment |
1
1
1
Something like this?
from collections import Counter
from spacy.matcher import PhraseMatcher
color_patterns = [nlp(text) for text in ('red', 'green', 'yellow')]
product_patterns = [nlp(text) for text in ('boots', 'coats', 'bag')]
material_patterns = [nlp(text) for text in ('bat', 'yellow ball')]
matcher = PhraseMatcher(nlp.vocab)
matcher.add('COLOR', None, *color_patterns)
matcher.add('PRODUCT', None, *product_patterns)
matcher.add('MATERIAL', None, *material_patterns)
d =
doc = nlp("yellow ball yellow lines")
matches = matcher(doc)
for match_id, start, end in matches:
rule_id = nlp.vocab.strings[match_id] # get the unicode ID, i.e. 'COLOR'
span = doc[start : end] # get the matched slice of the doc
d.append((rule_id, span.text))
print("n".join(f'{i[0]} {i[1]} ({j})' for i,j in Counter(d).items()))
Output:
COLOR yellow (2)
MATERIAL yellow ball (1)
answered Nov 25 '18 at 7:12
Srce CdeSrce Cde
1,144511
Something like this?
from collections import Counter
from spacy.matcher import PhraseMatcher
color_patterns = [nlp(text) for text in ('red', 'green', 'yellow')]
product_patterns = [nlp(text) for text in ('boots', 'coats', 'bag')]
material_patterns = [nlp(text) for text in ('bat', 'yellow ball')]
matcher = PhraseMatcher(nlp.vocab)
matcher.add('COLOR', None, *color_patterns)
matcher.add('PRODUCT', None, *product_patterns)
matcher.add('MATERIAL', None, *material_patterns)
d =
doc = nlp("yellow ball yellow lines")
matches = matcher(doc)
for match_id, start, end in matches:
rule_id = nlp.vocab.strings[match_id] # get the unicode ID, i.e. 'COLOR'
span = doc[start : end] # get the matched slice of the doc
d.append((rule_id, span.text))
print("n".join(f'{i[0]} {i[1]} ({j})' for i,j in Counter(d).items()))
Output:
COLOR yellow (2)
MATERIAL yellow ball (1)
answered Nov 25 '18 at 7:12
Srce CdeSrce Cde
1,144511
answered Nov 25 '18 at 7:12
Srce CdeSrce Cde
1,144511
answered Nov 25 '18 at 7:12
Srce CdeSrce Cde
1,144511
answered Nov 25 '18 at 7:12
Srce CdeSrce Cde
1,144511
1,144511
add a comment |
add a comment |
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