How to group text categorical data basis on clustering?
So if I have text dataset where I have more than 50 categories.
Sample:
Index_no short_text_sentence label
01 yes I like riding a bike. category_0
02 I was 4 when I learned. category_1
03 I learned to ride a bike. category_2
04 Bike is yellow and black. category_3
05 i like riding my bike, i learnt category_4
06 riding a bike when i was 8 or category_3
07 9 years old ,my bike is sparkling category_9
08 pink with white marks category_6
09 I love riding bike. category_1
10 I will improve my skills. category_3
Now I want to try RNN and LSTM over it but due to many categories ( 50+) I am not getting good result, because for each sentence probability is distributing across 50 categories.
I was thinking to group the categories basis on t-sne or other clustering methods but I am looking ways how to do in text and group those categories so I'll end up with few categories.
I am using tensorflow and my network structure is RNN-LSTM without attention. I am also thinking to go for CNN.
I would greatly appreciate it if anyone kindly give me some advice on how should I group the categories and which network structure should I choose for this type of problem?
Thanks in advance.
tensorflow machine-learning deep-learning classification multiclass-classification
asked Nov 24 '18 at 10:43
Aaditya UraAaditya Ura
4,79221533
add a comment |
So if I have text dataset where I have more than 50 categories.
Sample:
Index_no short_text_sentence label
01 yes I like riding a bike. category_0
02 I was 4 when I learned. category_1
03 I learned to ride a bike. category_2
04 Bike is yellow and black. category_3
05 i like riding my bike, i learnt category_4
06 riding a bike when i was 8 or category_3
07 9 years old ,my bike is sparkling category_9
08 pink with white marks category_6
09 I love riding bike. category_1
10 I will improve my skills. category_3
Now I want to try RNN and LSTM over it but due to many categories ( 50+) I am not getting good result, because for each sentence probability is distributing across 50 categories.
I was thinking to group the categories basis on t-sne or other clustering methods but I am looking ways how to do in text and group those categories so I'll end up with few categories.
I am using tensorflow and my network structure is RNN-LSTM without attention. I am also thinking to go for CNN.
I would greatly appreciate it if anyone kindly give me some advice on how should I group the categories and which network structure should I choose for this type of problem?
Thanks in advance.
tensorflow machine-learning deep-learning classification multiclass-classification
asked Nov 24 '18 at 10:43
Aaditya UraAaditya Ura
4,79221533
add a comment |
So if I have text dataset where I have more than 50 categories.
Sample:
Index_no short_text_sentence label
01 yes I like riding a bike. category_0
02 I was 4 when I learned. category_1
03 I learned to ride a bike. category_2
04 Bike is yellow and black. category_3
05 i like riding my bike, i learnt category_4
06 riding a bike when i was 8 or category_3
07 9 years old ,my bike is sparkling category_9
08 pink with white marks category_6
09 I love riding bike. category_1
10 I will improve my skills. category_3
Now I want to try RNN and LSTM over it but due to many categories ( 50+) I am not getting good result, because for each sentence probability is distributing across 50 categories.
I was thinking to group the categories basis on t-sne or other clustering methods but I am looking ways how to do in text and group those categories so I'll end up with few categories.
I am using tensorflow and my network structure is RNN-LSTM without attention. I am also thinking to go for CNN.
I would greatly appreciate it if anyone kindly give me some advice on how should I group the categories and which network structure should I choose for this type of problem?
Thanks in advance.
tensorflow machine-learning deep-learning classification multiclass-classification
asked Nov 24 '18 at 10:43
Aaditya UraAaditya Ura
4,79221533
So if I have text dataset where I have more than 50 categories.
Sample:
Index_no short_text_sentence label
01 yes I like riding a bike. category_0
02 I was 4 when I learned. category_1
03 I learned to ride a bike. category_2
04 Bike is yellow and black. category_3
05 i like riding my bike, i learnt category_4
06 riding a bike when i was 8 or category_3
07 9 years old ,my bike is sparkling category_9
08 pink with white marks category_6
09 I love riding bike. category_1
10 I will improve my skills. category_3
Now I want to try RNN and LSTM over it but due to many categories ( 50+) I am not getting good result, because for each sentence probability is distributing across 50 categories.
I was thinking to group the categories basis on t-sne or other clustering methods but I am looking ways how to do in text and group those categories so I'll end up with few categories.
I am using tensorflow and my network structure is RNN-LSTM without attention. I am also thinking to go for CNN.
I would greatly appreciate it if anyone kindly give me some advice on how should I group the categories and which network structure should I choose for this type of problem?
Thanks in advance.
tensorflow machine-learning deep-learning classification multiclass-classification
tensorflow machine-learning deep-learning classification multiclass-classification
asked Nov 24 '18 at 10:43
Aaditya UraAaditya Ura
4,79221533
asked Nov 24 '18 at 10:43
Aaditya UraAaditya Ura
4,79221533
edited Nov 26 '18 at 4:16
Aaditya Ura
asked Nov 24 '18 at 10:43
Aaditya UraAaditya Ura
4,79221533
asked Nov 24 '18 at 10:43
Aaditya UraAaditya Ura
4,79221533
asked Nov 24 '18 at 10:43
Aaditya UraAaditya Ura
4,79221533
4,79221533
add a comment |
add a comment |
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