Doc2vec '-' symbol occurrence
$begingroup$
Currently working on resume parser and struggled with embedding words with '-' symbols in them. Such as 'IT-manager'.
Vector representations of these words are incorrectly classified by doc2vec.
['it-manager']
[('salary', 0.23328335583209991), ('responsibilites', 0.22327110171318054), ('schedule', 0.14869527518749237), ('position', 0.12755176424980164)]
But when I remove '-' symbol, it is tokenized and classified right.
['it', 'manager']
[('position', 0.9306046962738037), ('schedule', 0.6630333662033081), ('responsibilites', 0.6081600189208984), ('salary', 0.5934453010559082)]
How do you work with such data properly? For this kind of task, I guess, it is better to exclude the symbol. But there may be a way to tell Doc2vec to treat these words like two different ones. Or perhaps tell the word_tokenizer to tokenize them in this fashion?
nlp word2vec word-embeddings nltk
edited 2 days ago
HFulcher
9513
asked 2 days ago
GraygoodGraygood
83
$endgroup$
add a comment |
$begingroup$
Currently working on resume parser and struggled with embedding words with '-' symbols in them. Such as 'IT-manager'.
Vector representations of these words are incorrectly classified by doc2vec.
['it-manager']
[('salary', 0.23328335583209991), ('responsibilites', 0.22327110171318054), ('schedule', 0.14869527518749237), ('position', 0.12755176424980164)]
But when I remove '-' symbol, it is tokenized and classified right.
['it', 'manager']
[('position', 0.9306046962738037), ('schedule', 0.6630333662033081), ('responsibilites', 0.6081600189208984), ('salary', 0.5934453010559082)]
How do you work with such data properly? For this kind of task, I guess, it is better to exclude the symbol. But there may be a way to tell Doc2vec to treat these words like two different ones. Or perhaps tell the word_tokenizer to tokenize them in this fashion?
nlp word2vec word-embeddings nltk
edited 2 days ago
HFulcher
9513
asked 2 days ago
GraygoodGraygood
83
$endgroup$
add a comment |
$begingroup$
Currently working on resume parser and struggled with embedding words with '-' symbols in them. Such as 'IT-manager'.
Vector representations of these words are incorrectly classified by doc2vec.
['it-manager']
[('salary', 0.23328335583209991), ('responsibilites', 0.22327110171318054), ('schedule', 0.14869527518749237), ('position', 0.12755176424980164)]
But when I remove '-' symbol, it is tokenized and classified right.
['it', 'manager']
[('position', 0.9306046962738037), ('schedule', 0.6630333662033081), ('responsibilites', 0.6081600189208984), ('salary', 0.5934453010559082)]
How do you work with such data properly? For this kind of task, I guess, it is better to exclude the symbol. But there may be a way to tell Doc2vec to treat these words like two different ones. Or perhaps tell the word_tokenizer to tokenize them in this fashion?
nlp word2vec word-embeddings nltk
edited 2 days ago
HFulcher
9513
asked 2 days ago
GraygoodGraygood
83
$endgroup$
Currently working on resume parser and struggled with embedding words with '-' symbols in them. Such as 'IT-manager'.
Vector representations of these words are incorrectly classified by doc2vec.
['it-manager']
[('salary', 0.23328335583209991), ('responsibilites', 0.22327110171318054), ('schedule', 0.14869527518749237), ('position', 0.12755176424980164)]
But when I remove '-' symbol, it is tokenized and classified right.
['it', 'manager']
[('position', 0.9306046962738037), ('schedule', 0.6630333662033081), ('responsibilites', 0.6081600189208984), ('salary', 0.5934453010559082)]
How do you work with such data properly? For this kind of task, I guess, it is better to exclude the symbol. But there may be a way to tell Doc2vec to treat these words like two different ones. Or perhaps tell the word_tokenizer to tokenize them in this fashion?
nlp word2vec word-embeddings nltk
nlp word2vec word-embeddings nltk
edited 2 days ago
HFulcher
9513
asked 2 days ago
GraygoodGraygood
83
edited 2 days ago
HFulcher
9513
asked 2 days ago
GraygoodGraygood
83
edited 2 days ago
HFulcher
9513
edited 2 days ago
HFulcher
9513
edited 2 days ago
HFulcher
9513
9513
asked 2 days ago
GraygoodGraygood
83
asked 2 days ago
GraygoodGraygood
83
asked 2 days ago
GraygoodGraygood
83
83
add a comment |
add a comment |
2 Answers
2
active
oldest
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$begingroup$
gensim's Phrases module may also be helpful:
from gensim.models import Phrases
documents = [
"the mayor of new york was there",
"machine learning can be useful sometimes",
"new york mayor was present"
]
sentence_stream = [doc.split(" ") for doc in documents]
bigram = Phrases(sentence_stream, min_count=1, threshold=2)
sent = [u'the', u'mayor', u'of', u'new', u'york', u'was', u'there']
print(bigram[sent])
# Expected output:
# [u'the', u'mayor', u'of', u'new_york', u'was', u'there']
That code is from this other answer (I've copy-pasted it above for convenience).
For more on the Phrases module, check this page out.
answered 12 hours ago
JordiCarreraJordiCarrera
261
New contributor
JordiCarrera 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 |
$begingroup$
Typically you would want to remove any symbols that do not contribute to the meaning of a token. In the case of 'it-manager' by removing the - you do not affect the interpretation of the word negatively. I would suggest filtering your vocabulary to identify all the words with other symbols in and make a judgement on whether you can filter the symbols without affecting the interpretation of the word.
You could do this using a regex filter such as:
m = re.search(r'[^w]', <some string>)
answered 2 days ago
HFulcherHFulcher
9513
$endgroup$
add a comment |
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2 Answers
2
active
oldest
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2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
gensim's Phrases module may also be helpful:
from gensim.models import Phrases
documents = [
"the mayor of new york was there",
"machine learning can be useful sometimes",
"new york mayor was present"
]
sentence_stream = [doc.split(" ") for doc in documents]
bigram = Phrases(sentence_stream, min_count=1, threshold=2)
sent = [u'the', u'mayor', u'of', u'new', u'york', u'was', u'there']
print(bigram[sent])
# Expected output:
# [u'the', u'mayor', u'of', u'new_york', u'was', u'there']
That code is from this other answer (I've copy-pasted it above for convenience).
For more on the Phrases module, check this page out.
answered 12 hours ago
JordiCarreraJordiCarrera
261
New contributor
JordiCarrera 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 |
$begingroup$
gensim's Phrases module may also be helpful:
from gensim.models import Phrases
documents = [
"the mayor of new york was there",
"machine learning can be useful sometimes",
"new york mayor was present"
]
sentence_stream = [doc.split(" ") for doc in documents]
bigram = Phrases(sentence_stream, min_count=1, threshold=2)
sent = [u'the', u'mayor', u'of', u'new', u'york', u'was', u'there']
print(bigram[sent])
# Expected output:
# [u'the', u'mayor', u'of', u'new_york', u'was', u'there']
That code is from this other answer (I've copy-pasted it above for convenience).
For more on the Phrases module, check this page out.
answered 12 hours ago
JordiCarreraJordiCarrera
261
New contributor
JordiCarrera 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 |
$begingroup$
gensim's Phrases module may also be helpful:
from gensim.models import Phrases
documents = [
"the mayor of new york was there",
"machine learning can be useful sometimes",
"new york mayor was present"
]
sentence_stream = [doc.split(" ") for doc in documents]
bigram = Phrases(sentence_stream, min_count=1, threshold=2)
sent = [u'the', u'mayor', u'of', u'new', u'york', u'was', u'there']
print(bigram[sent])
# Expected output:
# [u'the', u'mayor', u'of', u'new_york', u'was', u'there']
That code is from this other answer (I've copy-pasted it above for convenience).
For more on the Phrases module, check this page out.
answered 12 hours ago
JordiCarreraJordiCarrera
261
New contributor
JordiCarrera is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
gensim's Phrases module may also be helpful:
from gensim.models import Phrases
documents = [
"the mayor of new york was there",
"machine learning can be useful sometimes",
"new york mayor was present"
]
sentence_stream = [doc.split(" ") for doc in documents]
bigram = Phrases(sentence_stream, min_count=1, threshold=2)
sent = [u'the', u'mayor', u'of', u'new', u'york', u'was', u'there']
print(bigram[sent])
# Expected output:
# [u'the', u'mayor', u'of', u'new_york', u'was', u'there']
That code is from this other answer (I've copy-pasted it above for convenience).
For more on the Phrases module, check this page out.
answered 12 hours ago
JordiCarreraJordiCarrera
261
New contributor
JordiCarrera is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered 12 hours ago
JordiCarreraJordiCarrera
261
New contributor
JordiCarrera is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered 12 hours ago
JordiCarreraJordiCarrera
261
answered 12 hours ago
JordiCarreraJordiCarrera
261
261
New contributor
JordiCarrera is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
JordiCarrera is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
JordiCarrera 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 |
$begingroup$
Typically you would want to remove any symbols that do not contribute to the meaning of a token. In the case of 'it-manager' by removing the - you do not affect the interpretation of the word negatively. I would suggest filtering your vocabulary to identify all the words with other symbols in and make a judgement on whether you can filter the symbols without affecting the interpretation of the word.
You could do this using a regex filter such as:
m = re.search(r'[^w]', <some string>)
answered 2 days ago
HFulcherHFulcher
9513
$endgroup$
add a comment |
$begingroup$
Typically you would want to remove any symbols that do not contribute to the meaning of a token. In the case of 'it-manager' by removing the - you do not affect the interpretation of the word negatively. I would suggest filtering your vocabulary to identify all the words with other symbols in and make a judgement on whether you can filter the symbols without affecting the interpretation of the word.
You could do this using a regex filter such as:
m = re.search(r'[^w]', <some string>)
answered 2 days ago
HFulcherHFulcher
9513
$endgroup$
add a comment |
$begingroup$
Typically you would want to remove any symbols that do not contribute to the meaning of a token. In the case of 'it-manager' by removing the - you do not affect the interpretation of the word negatively. I would suggest filtering your vocabulary to identify all the words with other symbols in and make a judgement on whether you can filter the symbols without affecting the interpretation of the word.
You could do this using a regex filter such as:
m = re.search(r'[^w]', <some string>)
answered 2 days ago
HFulcherHFulcher
9513
$endgroup$
Typically you would want to remove any symbols that do not contribute to the meaning of a token. In the case of 'it-manager' by removing the - you do not affect the interpretation of the word negatively. I would suggest filtering your vocabulary to identify all the words with other symbols in and make a judgement on whether you can filter the symbols without affecting the interpretation of the word.
You could do this using a regex filter such as:
m = re.search(r'[^w]', <some string>)
answered 2 days ago
HFulcherHFulcher
9513
answered 2 days ago
HFulcherHFulcher
9513
answered 2 days ago
HFulcherHFulcher
9513
answered 2 days ago
HFulcherHFulcher
9513
9513
add a comment |
add a comment |
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