Which neural network to choose for classification from text/speech?CNN for classification giving extreme result probabilitiesPrediction interval around LSTM time series forecastHow to train neural network for text-to-speech task?Training an AI to play Starcraft 2 with superhuman level of performance?Using RNN (LSTM) for Gesture Recognition SystemKeras- LSTM answers different sizeword/sentence alignment for English documentMultiple-input multiple-output CNN with custom loss functionWord classification (not text classification) using NLPCan Convolutional Neural Networks (CNN) be represented by a Mathematical formula?
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Which neural network to choose for classification from text/speech?
CNN for classification giving extreme result probabilitiesPrediction interval around LSTM time series forecastHow to train neural network for text-to-speech task?Training an AI to play Starcraft 2 with superhuman level of performance?Using RNN (LSTM) for Gesture Recognition SystemKeras- LSTM answers different sizeword/sentence alignment for English documentMultiple-input multiple-output CNN with custom loss functionWord classification (not text classification) using NLPCan Convolutional Neural Networks (CNN) be represented by a Mathematical formula?
$begingroup$
I am considering two tasks:
- Dialog Act Classification from Text (e.g. classify to: question; opinion; ...)
- Emotion Recognition from Speech (e.g. happy; calm; sad; ...)
Which DL model should perform better for such tasks? I am planning to use CNN which should work for both of them, however not sure how well. Can I apply LSTM or some other methods? I used Keras before.
Is it good to apply attention mechanism or some other approaches for these 2 tasks?
neural-network deep-learning lstm cnn natural-language-process
asked Jan 20 at 19:39
G.H.G.H.
111
$endgroup$
bumped to the homepage by Community♦ 14 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 considering two tasks:
- Dialog Act Classification from Text (e.g. classify to: question; opinion; ...)
- Emotion Recognition from Speech (e.g. happy; calm; sad; ...)
Which DL model should perform better for such tasks? I am planning to use CNN which should work for both of them, however not sure how well. Can I apply LSTM or some other methods? I used Keras before.
Is it good to apply attention mechanism or some other approaches for these 2 tasks?
neural-network deep-learning lstm cnn natural-language-process
asked Jan 20 at 19:39
G.H.G.H.
111
$endgroup$
bumped to the homepage by Community♦ 14 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 considering two tasks:
- Dialog Act Classification from Text (e.g. classify to: question; opinion; ...)
- Emotion Recognition from Speech (e.g. happy; calm; sad; ...)
Which DL model should perform better for such tasks? I am planning to use CNN which should work for both of them, however not sure how well. Can I apply LSTM or some other methods? I used Keras before.
Is it good to apply attention mechanism or some other approaches for these 2 tasks?
neural-network deep-learning lstm cnn natural-language-process
asked Jan 20 at 19:39
G.H.G.H.
111
$endgroup$
I am considering two tasks:
- Dialog Act Classification from Text (e.g. classify to: question; opinion; ...)
- Emotion Recognition from Speech (e.g. happy; calm; sad; ...)
Which DL model should perform better for such tasks? I am planning to use CNN which should work for both of them, however not sure how well. Can I apply LSTM or some other methods? I used Keras before.
Is it good to apply attention mechanism or some other approaches for these 2 tasks?
neural-network deep-learning lstm cnn natural-language-process
neural-network deep-learning lstm cnn natural-language-process
asked Jan 20 at 19:39
G.H.G.H.
111
asked Jan 20 at 19:39
G.H.G.H.
111
asked Jan 20 at 19:39
G.H.G.H.
111
asked Jan 20 at 19:39
G.H.G.H.
111
asked Jan 20 at 19:39
G.H.G.H.
111
111
bumped to the homepage by Community♦ 14 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♦ 14 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 |
add a comment |
3 Answers
3
active
oldest
votes
$begingroup$
Welcome to the site. I'm a little disturbed by the other two answers you received here. It sounds like you are skipping a whole lot of steps and wanting to jump right into modeling - that's a massive mistake!
You are a scientist! Your role is to create the most fair, unbiased environment possible to let the data speak to you (not the other way around!). What worked before (LSTM) may or may not be the best approach to this completely new data set. Therefore, after doing your EDA phase, you should keep an "open field" view to the multiple models that you will examine and test prior to making any decisions about which model to proceed with. The answer may not even be a neural network, it may be a whole different approach.
Please, be responsible your data science practice. You cannot jump into modeling right away. Let the data speak to you.
answered Feb 20 at 22:29
I_Play_With_DataI_Play_With_Data
1,234532
$endgroup$
add a comment |
$begingroup$
You can use both 1D Convolutions and RNNs like LSTM for text classification task. It is hard to say which one is better because it depends on your neural network and dataset structure.
Take a smaller sample from your dataset, then train and evaluate both networks. Pick the best model. Train with bigger data on this model. I think the most convenient method is this.
I suggest you to read this and this articles to understand how LSTM works and what you can do with it. There are some examples and use cases. Decide is it appropriate for your data or not.
answered Jan 20 at 21:16
Abdüssamet ASLANAbdüssamet ASLAN
91
$endgroup$
$begingroup$
I'm disappointed by this answer. You make no inquiries as to the nature of their data but are willing to jump straight into modeling? That's not a sound methodology in data science.
$endgroup$
– I_Play_With_Data
Feb 20 at 22:30
$begingroup$
@I_Play_With_Data He did not ask the methodology, he asked for model alternatives. You can not judge anybody like this. Please read the question firstly.
$endgroup$
– Abdüssamet ASLAN
Feb 21 at 23:18
$begingroup$
Are you really trying to defend your use of bad methodology? I’m disappointed in you.
$endgroup$
– I_Play_With_Data
Feb 21 at 23:31
$begingroup$
@I_Play_With_Data My methodology is not best practice also please remember you are not a judge. You can feel disappointed about whatever you want. I only answered his question. He did not asked first steps, he asked for models. Please consider this website is not wikipedia, it is a q&a platform.
$endgroup$
– Abdüssamet ASLAN
Feb 21 at 23:39
$begingroup$
Very disappointed in you
$endgroup$
– I_Play_With_Data
Feb 21 at 23:45
add a comment |
$begingroup$
A typical model you could use is shown below-
Input Text -> Word Embedding -> Bidirectional LSTM -> Dense output layer
Word embedding layer - maps the words from the vocabulary into vectors of real numbers.
Bidirectional LSTM - since they can preserve information from both the past and the future they can understand context better as compared to unidirectional LSTM.
Checkout the following links for more details-
https://machinelearningmastery.com/what-are-word-embeddings/
https://machinelearningmastery.com/develop-bidirectional-lstm-sequence-classification-python-keras/
answered Jan 21 at 8:37
Amit RastogiAmit Rastogi
1744
$endgroup$
$begingroup$
I'm disappointed by this answer. You make no inquiries as to the nature of their data but are willing to jump straight into modeling? That's not a sound methodology in data science.
$endgroup$
– I_Play_With_Data
Feb 20 at 22:30
add a comment |
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3 Answers
3
active
oldest
votes
3 Answers
3
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
Welcome to the site. I'm a little disturbed by the other two answers you received here. It sounds like you are skipping a whole lot of steps and wanting to jump right into modeling - that's a massive mistake!
You are a scientist! Your role is to create the most fair, unbiased environment possible to let the data speak to you (not the other way around!). What worked before (LSTM) may or may not be the best approach to this completely new data set. Therefore, after doing your EDA phase, you should keep an "open field" view to the multiple models that you will examine and test prior to making any decisions about which model to proceed with. The answer may not even be a neural network, it may be a whole different approach.
Please, be responsible your data science practice. You cannot jump into modeling right away. Let the data speak to you.
answered Feb 20 at 22:29
I_Play_With_DataI_Play_With_Data
1,234532
$endgroup$
add a comment |
$begingroup$
Welcome to the site. I'm a little disturbed by the other two answers you received here. It sounds like you are skipping a whole lot of steps and wanting to jump right into modeling - that's a massive mistake!
You are a scientist! Your role is to create the most fair, unbiased environment possible to let the data speak to you (not the other way around!). What worked before (LSTM) may or may not be the best approach to this completely new data set. Therefore, after doing your EDA phase, you should keep an "open field" view to the multiple models that you will examine and test prior to making any decisions about which model to proceed with. The answer may not even be a neural network, it may be a whole different approach.
Please, be responsible your data science practice. You cannot jump into modeling right away. Let the data speak to you.
answered Feb 20 at 22:29
I_Play_With_DataI_Play_With_Data
1,234532
$endgroup$
add a comment |
$begingroup$
Welcome to the site. I'm a little disturbed by the other two answers you received here. It sounds like you are skipping a whole lot of steps and wanting to jump right into modeling - that's a massive mistake!
You are a scientist! Your role is to create the most fair, unbiased environment possible to let the data speak to you (not the other way around!). What worked before (LSTM) may or may not be the best approach to this completely new data set. Therefore, after doing your EDA phase, you should keep an "open field" view to the multiple models that you will examine and test prior to making any decisions about which model to proceed with. The answer may not even be a neural network, it may be a whole different approach.
Please, be responsible your data science practice. You cannot jump into modeling right away. Let the data speak to you.
answered Feb 20 at 22:29
I_Play_With_DataI_Play_With_Data
1,234532
$endgroup$
Welcome to the site. I'm a little disturbed by the other two answers you received here. It sounds like you are skipping a whole lot of steps and wanting to jump right into modeling - that's a massive mistake!
You are a scientist! Your role is to create the most fair, unbiased environment possible to let the data speak to you (not the other way around!). What worked before (LSTM) may or may not be the best approach to this completely new data set. Therefore, after doing your EDA phase, you should keep an "open field" view to the multiple models that you will examine and test prior to making any decisions about which model to proceed with. The answer may not even be a neural network, it may be a whole different approach.
Please, be responsible your data science practice. You cannot jump into modeling right away. Let the data speak to you.
answered Feb 20 at 22:29
I_Play_With_DataI_Play_With_Data
1,234532
answered Feb 20 at 22:29
I_Play_With_DataI_Play_With_Data
1,234532
answered Feb 20 at 22:29
I_Play_With_DataI_Play_With_Data
1,234532
answered Feb 20 at 22:29
I_Play_With_DataI_Play_With_Data
1,234532
1,234532
add a comment |
add a comment |
$begingroup$
You can use both 1D Convolutions and RNNs like LSTM for text classification task. It is hard to say which one is better because it depends on your neural network and dataset structure.
Take a smaller sample from your dataset, then train and evaluate both networks. Pick the best model. Train with bigger data on this model. I think the most convenient method is this.
I suggest you to read this and this articles to understand how LSTM works and what you can do with it. There are some examples and use cases. Decide is it appropriate for your data or not.
answered Jan 20 at 21:16
Abdüssamet ASLANAbdüssamet ASLAN
91
$endgroup$
$begingroup$
I'm disappointed by this answer. You make no inquiries as to the nature of their data but are willing to jump straight into modeling? That's not a sound methodology in data science.
$endgroup$
– I_Play_With_Data
Feb 20 at 22:30
$begingroup$
@I_Play_With_Data He did not ask the methodology, he asked for model alternatives. You can not judge anybody like this. Please read the question firstly.
$endgroup$
– Abdüssamet ASLAN
Feb 21 at 23:18
$begingroup$
Are you really trying to defend your use of bad methodology? I’m disappointed in you.
$endgroup$
– I_Play_With_Data
Feb 21 at 23:31
$begingroup$
@I_Play_With_Data My methodology is not best practice also please remember you are not a judge. You can feel disappointed about whatever you want. I only answered his question. He did not asked first steps, he asked for models. Please consider this website is not wikipedia, it is a q&a platform.
$endgroup$
– Abdüssamet ASLAN
Feb 21 at 23:39
$begingroup$
Very disappointed in you
$endgroup$
– I_Play_With_Data
Feb 21 at 23:45
add a comment |
$begingroup$
You can use both 1D Convolutions and RNNs like LSTM for text classification task. It is hard to say which one is better because it depends on your neural network and dataset structure.
Take a smaller sample from your dataset, then train and evaluate both networks. Pick the best model. Train with bigger data on this model. I think the most convenient method is this.
I suggest you to read this and this articles to understand how LSTM works and what you can do with it. There are some examples and use cases. Decide is it appropriate for your data or not.
answered Jan 20 at 21:16
Abdüssamet ASLANAbdüssamet ASLAN
91
$endgroup$
$begingroup$
I'm disappointed by this answer. You make no inquiries as to the nature of their data but are willing to jump straight into modeling? That's not a sound methodology in data science.
$endgroup$
– I_Play_With_Data
Feb 20 at 22:30
$begingroup$
@I_Play_With_Data He did not ask the methodology, he asked for model alternatives. You can not judge anybody like this. Please read the question firstly.
$endgroup$
– Abdüssamet ASLAN
Feb 21 at 23:18
$begingroup$
Are you really trying to defend your use of bad methodology? I’m disappointed in you.
$endgroup$
– I_Play_With_Data
Feb 21 at 23:31
$begingroup$
@I_Play_With_Data My methodology is not best practice also please remember you are not a judge. You can feel disappointed about whatever you want. I only answered his question. He did not asked first steps, he asked for models. Please consider this website is not wikipedia, it is a q&a platform.
$endgroup$
– Abdüssamet ASLAN
Feb 21 at 23:39
$begingroup$
Very disappointed in you
$endgroup$
– I_Play_With_Data
Feb 21 at 23:45
add a comment |
$begingroup$
You can use both 1D Convolutions and RNNs like LSTM for text classification task. It is hard to say which one is better because it depends on your neural network and dataset structure.
Take a smaller sample from your dataset, then train and evaluate both networks. Pick the best model. Train with bigger data on this model. I think the most convenient method is this.
I suggest you to read this and this articles to understand how LSTM works and what you can do with it. There are some examples and use cases. Decide is it appropriate for your data or not.
answered Jan 20 at 21:16
Abdüssamet ASLANAbdüssamet ASLAN
91
$endgroup$
You can use both 1D Convolutions and RNNs like LSTM for text classification task. It is hard to say which one is better because it depends on your neural network and dataset structure.
Take a smaller sample from your dataset, then train and evaluate both networks. Pick the best model. Train with bigger data on this model. I think the most convenient method is this.
I suggest you to read this and this articles to understand how LSTM works and what you can do with it. There are some examples and use cases. Decide is it appropriate for your data or not.
answered Jan 20 at 21:16
Abdüssamet ASLANAbdüssamet ASLAN
91
answered Jan 20 at 21:16
Abdüssamet ASLANAbdüssamet ASLAN
91
answered Jan 20 at 21:16
Abdüssamet ASLANAbdüssamet ASLAN
91
answered Jan 20 at 21:16
Abdüssamet ASLANAbdüssamet ASLAN
91
91
$begingroup$
I'm disappointed by this answer. You make no inquiries as to the nature of their data but are willing to jump straight into modeling? That's not a sound methodology in data science.
$endgroup$
– I_Play_With_Data
Feb 20 at 22:30
$begingroup$
@I_Play_With_Data He did not ask the methodology, he asked for model alternatives. You can not judge anybody like this. Please read the question firstly.
$endgroup$
– Abdüssamet ASLAN
Feb 21 at 23:18
$begingroup$
Are you really trying to defend your use of bad methodology? I’m disappointed in you.
$endgroup$
– I_Play_With_Data
Feb 21 at 23:31
$begingroup$
@I_Play_With_Data My methodology is not best practice also please remember you are not a judge. You can feel disappointed about whatever you want. I only answered his question. He did not asked first steps, he asked for models. Please consider this website is not wikipedia, it is a q&a platform.
$endgroup$
– Abdüssamet ASLAN
Feb 21 at 23:39
$begingroup$
Very disappointed in you
$endgroup$
– I_Play_With_Data
Feb 21 at 23:45
add a comment |
$begingroup$
I'm disappointed by this answer. You make no inquiries as to the nature of their data but are willing to jump straight into modeling? That's not a sound methodology in data science.
$endgroup$
– I_Play_With_Data
Feb 20 at 22:30
$begingroup$
@I_Play_With_Data He did not ask the methodology, he asked for model alternatives. You can not judge anybody like this. Please read the question firstly.
$endgroup$
– Abdüssamet ASLAN
Feb 21 at 23:18
$begingroup$
Are you really trying to defend your use of bad methodology? I’m disappointed in you.
$endgroup$
– I_Play_With_Data
Feb 21 at 23:31
$begingroup$
@I_Play_With_Data My methodology is not best practice also please remember you are not a judge. You can feel disappointed about whatever you want. I only answered his question. He did not asked first steps, he asked for models. Please consider this website is not wikipedia, it is a q&a platform.
$endgroup$
– Abdüssamet ASLAN
Feb 21 at 23:39
$begingroup$
Very disappointed in you
$endgroup$
– I_Play_With_Data
Feb 21 at 23:45
$begingroup$
I'm disappointed by this answer. You make no inquiries as to the nature of their data but are willing to jump straight into modeling? That's not a sound methodology in data science.
$endgroup$
– I_Play_With_Data
Feb 20 at 22:30
$begingroup$
I'm disappointed by this answer. You make no inquiries as to the nature of their data but are willing to jump straight into modeling? That's not a sound methodology in data science.
$endgroup$
– I_Play_With_Data
Feb 20 at 22:30
$begingroup$
@I_Play_With_Data He did not ask the methodology, he asked for model alternatives. You can not judge anybody like this. Please read the question firstly.
$endgroup$
– Abdüssamet ASLAN
Feb 21 at 23:18
$begingroup$
@I_Play_With_Data He did not ask the methodology, he asked for model alternatives. You can not judge anybody like this. Please read the question firstly.
$endgroup$
– Abdüssamet ASLAN
Feb 21 at 23:18
$begingroup$
Are you really trying to defend your use of bad methodology? I’m disappointed in you.
$endgroup$
– I_Play_With_Data
Feb 21 at 23:31
$begingroup$
Are you really trying to defend your use of bad methodology? I’m disappointed in you.
$endgroup$
– I_Play_With_Data
Feb 21 at 23:31
$begingroup$
@I_Play_With_Data My methodology is not best practice also please remember you are not a judge. You can feel disappointed about whatever you want. I only answered his question. He did not asked first steps, he asked for models. Please consider this website is not wikipedia, it is a q&a platform.
$endgroup$
– Abdüssamet ASLAN
Feb 21 at 23:39
$begingroup$
@I_Play_With_Data My methodology is not best practice also please remember you are not a judge. You can feel disappointed about whatever you want. I only answered his question. He did not asked first steps, he asked for models. Please consider this website is not wikipedia, it is a q&a platform.
$endgroup$
– Abdüssamet ASLAN
Feb 21 at 23:39
$begingroup$
Very disappointed in you
$endgroup$
– I_Play_With_Data
Feb 21 at 23:45
$begingroup$
Very disappointed in you
$endgroup$
– I_Play_With_Data
Feb 21 at 23:45
add a comment |
$begingroup$
A typical model you could use is shown below-
Input Text -> Word Embedding -> Bidirectional LSTM -> Dense output layer
Word embedding layer - maps the words from the vocabulary into vectors of real numbers.
Bidirectional LSTM - since they can preserve information from both the past and the future they can understand context better as compared to unidirectional LSTM.
Checkout the following links for more details-
https://machinelearningmastery.com/what-are-word-embeddings/
https://machinelearningmastery.com/develop-bidirectional-lstm-sequence-classification-python-keras/
answered Jan 21 at 8:37
Amit RastogiAmit Rastogi
1744
$endgroup$
$begingroup$
I'm disappointed by this answer. You make no inquiries as to the nature of their data but are willing to jump straight into modeling? That's not a sound methodology in data science.
$endgroup$
– I_Play_With_Data
Feb 20 at 22:30
add a comment |
$begingroup$
A typical model you could use is shown below-
Input Text -> Word Embedding -> Bidirectional LSTM -> Dense output layer
Word embedding layer - maps the words from the vocabulary into vectors of real numbers.
Bidirectional LSTM - since they can preserve information from both the past and the future they can understand context better as compared to unidirectional LSTM.
Checkout the following links for more details-
https://machinelearningmastery.com/what-are-word-embeddings/
https://machinelearningmastery.com/develop-bidirectional-lstm-sequence-classification-python-keras/
answered Jan 21 at 8:37
Amit RastogiAmit Rastogi
1744
$endgroup$
$begingroup$
I'm disappointed by this answer. You make no inquiries as to the nature of their data but are willing to jump straight into modeling? That's not a sound methodology in data science.
$endgroup$
– I_Play_With_Data
Feb 20 at 22:30
add a comment |
$begingroup$
A typical model you could use is shown below-
Input Text -> Word Embedding -> Bidirectional LSTM -> Dense output layer
Word embedding layer - maps the words from the vocabulary into vectors of real numbers.
Bidirectional LSTM - since they can preserve information from both the past and the future they can understand context better as compared to unidirectional LSTM.
Checkout the following links for more details-
https://machinelearningmastery.com/what-are-word-embeddings/
https://machinelearningmastery.com/develop-bidirectional-lstm-sequence-classification-python-keras/
answered Jan 21 at 8:37
Amit RastogiAmit Rastogi
1744
$endgroup$
A typical model you could use is shown below-
Input Text -> Word Embedding -> Bidirectional LSTM -> Dense output layer
Word embedding layer - maps the words from the vocabulary into vectors of real numbers.
Bidirectional LSTM - since they can preserve information from both the past and the future they can understand context better as compared to unidirectional LSTM.
Checkout the following links for more details-
https://machinelearningmastery.com/what-are-word-embeddings/
https://machinelearningmastery.com/develop-bidirectional-lstm-sequence-classification-python-keras/
answered Jan 21 at 8:37
Amit RastogiAmit Rastogi
1744
answered Jan 21 at 8:37
Amit RastogiAmit Rastogi
1744
answered Jan 21 at 8:37
Amit RastogiAmit Rastogi
1744
answered Jan 21 at 8:37
Amit RastogiAmit Rastogi
1744
1744
$begingroup$
I'm disappointed by this answer. You make no inquiries as to the nature of their data but are willing to jump straight into modeling? That's not a sound methodology in data science.
$endgroup$
– I_Play_With_Data
Feb 20 at 22:30
add a comment |
$begingroup$
I'm disappointed by this answer. You make no inquiries as to the nature of their data but are willing to jump straight into modeling? That's not a sound methodology in data science.
$endgroup$
– I_Play_With_Data
Feb 20 at 22:30
$begingroup$
I'm disappointed by this answer. You make no inquiries as to the nature of their data but are willing to jump straight into modeling? That's not a sound methodology in data science.
$endgroup$
– I_Play_With_Data
Feb 20 at 22:30
$begingroup$
I'm disappointed by this answer. You make no inquiries as to the nature of their data but are willing to jump straight into modeling? That's not a sound methodology in data science.
$endgroup$
– I_Play_With_Data
Feb 20 at 22:30
add a comment |
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