Efficient way to replace incorrect words in Series of strings
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I'm working with text data, that is handwritten, so it has lots of ortographic errors. I'm currently working with pyspellchecker to clean the data and I'm using the correct() method to find the most likely word when a word doesn't exist. My approach was to create a dictionary with all poorly written words as keys and the most likely word as value:
dic={}
for i in df.text:
misspelled = spell.unknown(i.split())
for word in misspelled:
dic[word]=spell.correction(word)
Even though this is working, it is doing so very slowly. Thus, I wanted to know if there's a faster option to implement this. Do you have any ideas?
python nlp
asked 1 hour ago
Juan CJuan C
1062
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Juan C is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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I'm working with text data, that is handwritten, so it has lots of ortographic errors. I'm currently working with pyspellchecker to clean the data and I'm using the correct() method to find the most likely word when a word doesn't exist. My approach was to create a dictionary with all poorly written words as keys and the most likely word as value:
dic={}
for i in df.text:
misspelled = spell.unknown(i.split())
for word in misspelled:
dic[word]=spell.correction(word)
Even though this is working, it is doing so very slowly. Thus, I wanted to know if there's a faster option to implement this. Do you have any ideas?
python nlp
asked 1 hour ago
Juan CJuan C
1062
New contributor
Juan C is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I'm working with text data, that is handwritten, so it has lots of ortographic errors. I'm currently working with pyspellchecker to clean the data and I'm using the correct() method to find the most likely word when a word doesn't exist. My approach was to create a dictionary with all poorly written words as keys and the most likely word as value:
dic={}
for i in df.text:
misspelled = spell.unknown(i.split())
for word in misspelled:
dic[word]=spell.correction(word)
Even though this is working, it is doing so very slowly. Thus, I wanted to know if there's a faster option to implement this. Do you have any ideas?
python nlp
asked 1 hour ago
Juan CJuan C
1062
New contributor
Juan C is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I'm working with text data, that is handwritten, so it has lots of ortographic errors. I'm currently working with pyspellchecker to clean the data and I'm using the correct() method to find the most likely word when a word doesn't exist. My approach was to create a dictionary with all poorly written words as keys and the most likely word as value:
dic={}
for i in df.text:
misspelled = spell.unknown(i.split())
for word in misspelled:
dic[word]=spell.correction(word)
Even though this is working, it is doing so very slowly. Thus, I wanted to know if there's a faster option to implement this. Do you have any ideas?
python nlp
python nlp
asked 1 hour ago
Juan CJuan C
1062
New contributor
Juan C is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 1 hour ago
Juan CJuan C
1062
New contributor
Juan C is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 1 hour ago
Juan CJuan C
1062
New contributor
Juan C is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 1 hour ago
Juan CJuan C
1062
asked 1 hour ago
Juan CJuan C
1062
1062
New contributor
Juan C is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Juan C is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Juan C is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
add a comment |
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