How to identify noun that refers to a person
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How do I use NLP to determine whether a common noun (as opposed to a proper noun or proper name) refers to a person? Word vectors seem promising, but my naive attempts to use the similarity score from Gensim's and spaCy's pre-trained models failed to rank "engineer" above "dog" or even an abstract concept like "effort".
Gensim:
import gensim.downloader as api
word_vectors = api.load("glove-wiki-gigaword-100")
for word in ('effort', 'dog', 'engineer'):
print(word, word_vectors.similarity(word, 'person'))
# effort 0.42303842
# dog 0.46886832
# engineer 0.32456854
spaCy:
import pandas as pd
import spacy
nlp = spacy.load('en_core_web_lg')
tokens = [nlp(x) for x in "effort dog engineer".split()]
person = nlp("person")
for token in tokens:
print(token, token.similarity(person))
# effort 0.32646408671423605
# dog 0.37567509921825676
# engineer 0.27926451418425996
If I need to train a model myself, how should I go about it?
nlp gensim
asked 4 hours ago
bongbangbongbang
101
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$begingroup$
How do I use NLP to determine whether a common noun (as opposed to a proper noun or proper name) refers to a person? Word vectors seem promising, but my naive attempts to use the similarity score from Gensim's and spaCy's pre-trained models failed to rank "engineer" above "dog" or even an abstract concept like "effort".
Gensim:
import gensim.downloader as api
word_vectors = api.load("glove-wiki-gigaword-100")
for word in ('effort', 'dog', 'engineer'):
print(word, word_vectors.similarity(word, 'person'))
# effort 0.42303842
# dog 0.46886832
# engineer 0.32456854
spaCy:
import pandas as pd
import spacy
nlp = spacy.load('en_core_web_lg')
tokens = [nlp(x) for x in "effort dog engineer".split()]
person = nlp("person")
for token in tokens:
print(token, token.similarity(person))
# effort 0.32646408671423605
# dog 0.37567509921825676
# engineer 0.27926451418425996
If I need to train a model myself, how should I go about it?
nlp gensim
asked 4 hours ago
bongbangbongbang
101
New contributor
bongbang 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 |
$begingroup$
How do I use NLP to determine whether a common noun (as opposed to a proper noun or proper name) refers to a person? Word vectors seem promising, but my naive attempts to use the similarity score from Gensim's and spaCy's pre-trained models failed to rank "engineer" above "dog" or even an abstract concept like "effort".
Gensim:
import gensim.downloader as api
word_vectors = api.load("glove-wiki-gigaword-100")
for word in ('effort', 'dog', 'engineer'):
print(word, word_vectors.similarity(word, 'person'))
# effort 0.42303842
# dog 0.46886832
# engineer 0.32456854
spaCy:
import pandas as pd
import spacy
nlp = spacy.load('en_core_web_lg')
tokens = [nlp(x) for x in "effort dog engineer".split()]
person = nlp("person")
for token in tokens:
print(token, token.similarity(person))
# effort 0.32646408671423605
# dog 0.37567509921825676
# engineer 0.27926451418425996
If I need to train a model myself, how should I go about it?
nlp gensim
asked 4 hours ago
bongbangbongbang
101
New contributor
bongbang is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
How do I use NLP to determine whether a common noun (as opposed to a proper noun or proper name) refers to a person? Word vectors seem promising, but my naive attempts to use the similarity score from Gensim's and spaCy's pre-trained models failed to rank "engineer" above "dog" or even an abstract concept like "effort".
Gensim:
import gensim.downloader as api
word_vectors = api.load("glove-wiki-gigaword-100")
for word in ('effort', 'dog', 'engineer'):
print(word, word_vectors.similarity(word, 'person'))
# effort 0.42303842
# dog 0.46886832
# engineer 0.32456854
spaCy:
import pandas as pd
import spacy
nlp = spacy.load('en_core_web_lg')
tokens = [nlp(x) for x in "effort dog engineer".split()]
person = nlp("person")
for token in tokens:
print(token, token.similarity(person))
# effort 0.32646408671423605
# dog 0.37567509921825676
# engineer 0.27926451418425996
If I need to train a model myself, how should I go about it?
nlp gensim
nlp gensim
asked 4 hours ago
bongbangbongbang
101
New contributor
bongbang is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 4 hours ago
bongbangbongbang
101
New contributor
bongbang is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 4 hours ago
bongbangbongbang
101
New contributor
bongbang is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 4 hours ago
bongbangbongbang
101
asked 4 hours ago
bongbangbongbang
101
101
New contributor
bongbang is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
bongbang is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
bongbang is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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1 Answer
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This problem is known as "coreference resslution" in NLP models.
There are two approached that might work here :
- Pre-trained coreference resolution models [For example : https://stanfordnlp.github.io/CoreNLP/coref.html and Spacy]. Though example code is in Java, similar Python wrappers are available for CoreNLP. Pre-trained models might be geared towards pronouns. If you have to train your own model, this page has HOWTO : https://stanfordnlp.github.io/CoreNLP/coref.html
- Dependency parsing (For example "Jane, my sister, wants to be an air hostess."). (Google Cloud, Spacy, CoreNLP)
Output of : https://cloud.google.com/natural-language/
In this case, parser is able to establish a relationship between tokens "sister" and "Jane".
answered 17 mins ago
Shamit VermaShamit Verma
1,4291214
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1 Answer
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$begingroup$
This problem is known as "coreference resslution" in NLP models.
There are two approached that might work here :
- Pre-trained coreference resolution models [For example : https://stanfordnlp.github.io/CoreNLP/coref.html and Spacy]. Though example code is in Java, similar Python wrappers are available for CoreNLP. Pre-trained models might be geared towards pronouns. If you have to train your own model, this page has HOWTO : https://stanfordnlp.github.io/CoreNLP/coref.html
- Dependency parsing (For example "Jane, my sister, wants to be an air hostess."). (Google Cloud, Spacy, CoreNLP)
Output of : https://cloud.google.com/natural-language/
In this case, parser is able to establish a relationship between tokens "sister" and "Jane".
answered 17 mins ago
Shamit VermaShamit Verma
1,4291214
$endgroup$
add a comment |
$begingroup$
This problem is known as "coreference resslution" in NLP models.
There are two approached that might work here :
- Pre-trained coreference resolution models [For example : https://stanfordnlp.github.io/CoreNLP/coref.html and Spacy]. Though example code is in Java, similar Python wrappers are available for CoreNLP. Pre-trained models might be geared towards pronouns. If you have to train your own model, this page has HOWTO : https://stanfordnlp.github.io/CoreNLP/coref.html
- Dependency parsing (For example "Jane, my sister, wants to be an air hostess."). (Google Cloud, Spacy, CoreNLP)
Output of : https://cloud.google.com/natural-language/
In this case, parser is able to establish a relationship between tokens "sister" and "Jane".
answered 17 mins ago
Shamit VermaShamit Verma
1,4291214
$endgroup$
add a comment |
$begingroup$
This problem is known as "coreference resslution" in NLP models.
There are two approached that might work here :
- Pre-trained coreference resolution models [For example : https://stanfordnlp.github.io/CoreNLP/coref.html and Spacy]. Though example code is in Java, similar Python wrappers are available for CoreNLP. Pre-trained models might be geared towards pronouns. If you have to train your own model, this page has HOWTO : https://stanfordnlp.github.io/CoreNLP/coref.html
- Dependency parsing (For example "Jane, my sister, wants to be an air hostess."). (Google Cloud, Spacy, CoreNLP)
Output of : https://cloud.google.com/natural-language/
In this case, parser is able to establish a relationship between tokens "sister" and "Jane".
answered 17 mins ago
Shamit VermaShamit Verma
1,4291214
$endgroup$
This problem is known as "coreference resslution" in NLP models.
There are two approached that might work here :
- Pre-trained coreference resolution models [For example : https://stanfordnlp.github.io/CoreNLP/coref.html and Spacy]. Though example code is in Java, similar Python wrappers are available for CoreNLP. Pre-trained models might be geared towards pronouns. If you have to train your own model, this page has HOWTO : https://stanfordnlp.github.io/CoreNLP/coref.html
- Dependency parsing (For example "Jane, my sister, wants to be an air hostess."). (Google Cloud, Spacy, CoreNLP)
Output of : https://cloud.google.com/natural-language/
In this case, parser is able to establish a relationship between tokens "sister" and "Jane".
answered 17 mins ago
Shamit VermaShamit Verma
1,4291214
answered 17 mins ago
Shamit VermaShamit Verma
1,4291214
answered 17 mins ago
Shamit VermaShamit Verma
1,4291214
answered 17 mins ago
Shamit VermaShamit Verma
1,4291214
1,4291214
add a comment |
add a comment |
bongbang is a new contributor. Be nice, and check out our Code of Conduct.
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