How to add extra word features other then word Embedding in Recurrent Neural Network model
$begingroup$
I am building a deep learning model for NLP. I am pretty comfortable with adding word embedding from word2vec or Glove vectors as extra word features but I wanted to add other word features like POS tag of a word, NER tag of word along with embedding as features. How can I do this. Should I give these word features by concatenating their vector with the word vectors. Or is there some other method. Please suggest.
deep-learning nlp rnn
asked Apr 28 '17 at 14:51
PrayalankarPrayalankar
877
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bumped to the homepage by Community♦ 2 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
I am building a deep learning model for NLP. I am pretty comfortable with adding word embedding from word2vec or Glove vectors as extra word features but I wanted to add other word features like POS tag of a word, NER tag of word along with embedding as features. How can I do this. Should I give these word features by concatenating their vector with the word vectors. Or is there some other method. Please suggest.
deep-learning nlp rnn
asked Apr 28 '17 at 14:51
PrayalankarPrayalankar
877
$endgroup$
bumped to the homepage by Community♦ 2 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
1
$begingroup$
Yes, concatenate them.
$endgroup$
– Emre
Apr 28 '17 at 21:52
add a comment |
$begingroup$
I am building a deep learning model for NLP. I am pretty comfortable with adding word embedding from word2vec or Glove vectors as extra word features but I wanted to add other word features like POS tag of a word, NER tag of word along with embedding as features. How can I do this. Should I give these word features by concatenating their vector with the word vectors. Or is there some other method. Please suggest.
deep-learning nlp rnn
asked Apr 28 '17 at 14:51
PrayalankarPrayalankar
877
$endgroup$
I am building a deep learning model for NLP. I am pretty comfortable with adding word embedding from word2vec or Glove vectors as extra word features but I wanted to add other word features like POS tag of a word, NER tag of word along with embedding as features. How can I do this. Should I give these word features by concatenating their vector with the word vectors. Or is there some other method. Please suggest.
deep-learning nlp rnn
deep-learning nlp rnn
asked Apr 28 '17 at 14:51
PrayalankarPrayalankar
877
asked Apr 28 '17 at 14:51
PrayalankarPrayalankar
877
asked Apr 28 '17 at 14:51
PrayalankarPrayalankar
877
asked Apr 28 '17 at 14:51
PrayalankarPrayalankar
877
asked Apr 28 '17 at 14:51
PrayalankarPrayalankar
877
877
bumped to the homepage by Community♦ 2 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ 2 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
1
$begingroup$
Yes, concatenate them.
$endgroup$
– Emre
Apr 28 '17 at 21:52
add a comment |
1
$begingroup$
Yes, concatenate them.
$endgroup$
– Emre
Apr 28 '17 at 21:52
1
1
$begingroup$
Yes, concatenate them.
$endgroup$
– Emre
Apr 28 '17 at 21:52
$begingroup$
Yes, concatenate them.
$endgroup$
– Emre
Apr 28 '17 at 21:52
add a comment |
2 Answers
2
active
oldest
votes
$begingroup$
One option is to concatenate them, the second is to treat them as separate inputs. For example Keras offers such neural model: https://keras.io/getting-started/functional-api-guide/#multi-input-and-multi-output-models
answered Jun 22 '18 at 10:42
djstrongdjstrong
212
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add a comment |
$begingroup$
I would concatenate them into a single input vector.
Essentially, your model treats each latent variable from the word embedding as a single feature (think about a regular ML model). Adding a couple to the end of this wouldn't hurt your performance too much.
Another option is to follow what @djstrong said, about multi-inputs. But I would start with just concatenating the extra variables at the end of your input vector.
answered Dec 20 '18 at 15:16
Arthur CamaraArthur Camara
101
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add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
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active
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votes
$begingroup$
One option is to concatenate them, the second is to treat them as separate inputs. For example Keras offers such neural model: https://keras.io/getting-started/functional-api-guide/#multi-input-and-multi-output-models
answered Jun 22 '18 at 10:42
djstrongdjstrong
212
$endgroup$
add a comment |
$begingroup$
One option is to concatenate them, the second is to treat them as separate inputs. For example Keras offers such neural model: https://keras.io/getting-started/functional-api-guide/#multi-input-and-multi-output-models
answered Jun 22 '18 at 10:42
djstrongdjstrong
212
$endgroup$
add a comment |
$begingroup$
One option is to concatenate them, the second is to treat them as separate inputs. For example Keras offers such neural model: https://keras.io/getting-started/functional-api-guide/#multi-input-and-multi-output-models
answered Jun 22 '18 at 10:42
djstrongdjstrong
212
$endgroup$
One option is to concatenate them, the second is to treat them as separate inputs. For example Keras offers such neural model: https://keras.io/getting-started/functional-api-guide/#multi-input-and-multi-output-models
answered Jun 22 '18 at 10:42
djstrongdjstrong
212
answered Jun 22 '18 at 10:42
djstrongdjstrong
212
answered Jun 22 '18 at 10:42
djstrongdjstrong
212
answered Jun 22 '18 at 10:42
djstrongdjstrong
212
212
add a comment |
add a comment |
$begingroup$
I would concatenate them into a single input vector.
Essentially, your model treats each latent variable from the word embedding as a single feature (think about a regular ML model). Adding a couple to the end of this wouldn't hurt your performance too much.
Another option is to follow what @djstrong said, about multi-inputs. But I would start with just concatenating the extra variables at the end of your input vector.
answered Dec 20 '18 at 15:16
Arthur CamaraArthur Camara
101
$endgroup$
add a comment |
$begingroup$
I would concatenate them into a single input vector.
Essentially, your model treats each latent variable from the word embedding as a single feature (think about a regular ML model). Adding a couple to the end of this wouldn't hurt your performance too much.
Another option is to follow what @djstrong said, about multi-inputs. But I would start with just concatenating the extra variables at the end of your input vector.
answered Dec 20 '18 at 15:16
Arthur CamaraArthur Camara
101
$endgroup$
add a comment |
$begingroup$
I would concatenate them into a single input vector.
Essentially, your model treats each latent variable from the word embedding as a single feature (think about a regular ML model). Adding a couple to the end of this wouldn't hurt your performance too much.
Another option is to follow what @djstrong said, about multi-inputs. But I would start with just concatenating the extra variables at the end of your input vector.
answered Dec 20 '18 at 15:16
Arthur CamaraArthur Camara
101
$endgroup$
I would concatenate them into a single input vector.
Essentially, your model treats each latent variable from the word embedding as a single feature (think about a regular ML model). Adding a couple to the end of this wouldn't hurt your performance too much.
Another option is to follow what @djstrong said, about multi-inputs. But I would start with just concatenating the extra variables at the end of your input vector.
answered Dec 20 '18 at 15:16
Arthur CamaraArthur Camara
101
answered Dec 20 '18 at 15:16
Arthur CamaraArthur Camara
101
answered Dec 20 '18 at 15:16
Arthur CamaraArthur Camara
101
answered Dec 20 '18 at 15:16
Arthur CamaraArthur Camara
101
101
add a comment |
add a comment |
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$begingroup$
Yes, concatenate them.
$endgroup$
– Emre
Apr 28 '17 at 21:52