How to cluster text-based software requirementsKeyword/phrase extraction from Text using Deep Learning librariesHow can autoencoders be used for clustering?Encog neural network multiple outputsOne hot encoding vs Word embeddingKeyword Extraction from a text followed by a key value using tensorflowGraph & Network Mining: clustering/community detection/ classificationDeep Learning Network decreasing in accuracyHow Do I Learn Neural Networks?Neural Network for detecting/checking for requirements in diagramsWhy is MLP working similar to RNN for text generation
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How to cluster text-based software requirements
Keyword/phrase extraction from Text using Deep Learning librariesHow can autoencoders be used for clustering?Encog neural network multiple outputsOne hot encoding vs Word embeddingKeyword Extraction from a text followed by a key value using tensorflowGraph & Network Mining: clustering/community detection/ classificationDeep Learning Network decreasing in accuracyHow Do I Learn Neural Networks?Neural Network for detecting/checking for requirements in diagramsWhy is MLP working similar to RNN for text generation
$begingroup$
I'm beginner in deep learning and I'd like to cluster text-based software requirements by themes (words similarities/frequency of words) using neural networks. Is there any example/tutorial/github code of unsupervised neural network that groups texts based on themes and words similarities?
Thank you very much for your answers!
neural-network clustering unsupervised-learning natural-language-process
asked Apr 9 at 16:40
TakwaTakwa
62
$endgroup$
add a comment |
$begingroup$
I'm beginner in deep learning and I'd like to cluster text-based software requirements by themes (words similarities/frequency of words) using neural networks. Is there any example/tutorial/github code of unsupervised neural network that groups texts based on themes and words similarities?
Thank you very much for your answers!
neural-network clustering unsupervised-learning natural-language-process
asked Apr 9 at 16:40
TakwaTakwa
62
$endgroup$
add a comment |
$begingroup$
I'm beginner in deep learning and I'd like to cluster text-based software requirements by themes (words similarities/frequency of words) using neural networks. Is there any example/tutorial/github code of unsupervised neural network that groups texts based on themes and words similarities?
Thank you very much for your answers!
neural-network clustering unsupervised-learning natural-language-process
asked Apr 9 at 16:40
TakwaTakwa
62
$endgroup$
I'm beginner in deep learning and I'd like to cluster text-based software requirements by themes (words similarities/frequency of words) using neural networks. Is there any example/tutorial/github code of unsupervised neural network that groups texts based on themes and words similarities?
Thank you very much for your answers!
neural-network clustering unsupervised-learning natural-language-process
neural-network clustering unsupervised-learning natural-language-process
asked Apr 9 at 16:40
TakwaTakwa
62
asked Apr 9 at 16:40
TakwaTakwa
62
asked Apr 9 at 16:40
TakwaTakwa
62
asked Apr 9 at 16:40
TakwaTakwa
62
asked Apr 9 at 16:40
TakwaTakwa
62
62
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
I recommend using word2vec as feature vector of words and LSTM autoencoder to encode a sentence (or text) . After you get a vector for each sentence (or text), you can cluster your sentences (or texts) using a variety of clustering techniques like k-means or dbscan and represent them using t-sne or u-map. Start from here:
https://blog.myyellowroad.com/unsupervised-sentence-representation-with-deep-learning-104b90079a93
answered Apr 9 at 17:06
pythinkerpythinker
8641314
$endgroup$
$begingroup$
thank you for your answer ! regarding the sentence encoding, there is an existing implementation of the TF-IDF algorithm in sklearn, here is the tutorial (pythonprogramminglanguage.com/kmeans-text-clustering). Thus, i am wondering why it's recommended to use encoding techniques such as word2vec and LSTM. Can you please explain the advantages of using such techniques compared to the one implemented in sklearn for instance?
$endgroup$
– Takwa
Apr 19 at 14:12
$begingroup$
You’re welcome. Actually, the first advantage of using word2vec over tf-idf is that, word2vec contains contextual information but tf-idf does not. The second advantage is that, word2vec uses information from a large dataset (pre-training), so it better models the language than tf-idf. And for the third advantage you should consider that as the vocabulary size increases, the tf-idf size increases, too. However, pre-trained word2vec vectors have fixed size, regardless of vocabulary size.
$endgroup$
– pythinker
Apr 19 at 18:45
$begingroup$
Thank you for the explanation @pythinker !
$endgroup$
– Takwa
Apr 25 at 8:21
$begingroup$
@Takwa You’re welcome
$endgroup$
– pythinker
Apr 25 at 9:19
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
I recommend using word2vec as feature vector of words and LSTM autoencoder to encode a sentence (or text) . After you get a vector for each sentence (or text), you can cluster your sentences (or texts) using a variety of clustering techniques like k-means or dbscan and represent them using t-sne or u-map. Start from here:
https://blog.myyellowroad.com/unsupervised-sentence-representation-with-deep-learning-104b90079a93
answered Apr 9 at 17:06
pythinkerpythinker
8641314
$endgroup$
$begingroup$
thank you for your answer ! regarding the sentence encoding, there is an existing implementation of the TF-IDF algorithm in sklearn, here is the tutorial (pythonprogramminglanguage.com/kmeans-text-clustering). Thus, i am wondering why it's recommended to use encoding techniques such as word2vec and LSTM. Can you please explain the advantages of using such techniques compared to the one implemented in sklearn for instance?
$endgroup$
– Takwa
Apr 19 at 14:12
$begingroup$
You’re welcome. Actually, the first advantage of using word2vec over tf-idf is that, word2vec contains contextual information but tf-idf does not. The second advantage is that, word2vec uses information from a large dataset (pre-training), so it better models the language than tf-idf. And for the third advantage you should consider that as the vocabulary size increases, the tf-idf size increases, too. However, pre-trained word2vec vectors have fixed size, regardless of vocabulary size.
$endgroup$
– pythinker
Apr 19 at 18:45
$begingroup$
Thank you for the explanation @pythinker !
$endgroup$
– Takwa
Apr 25 at 8:21
$begingroup$
@Takwa You’re welcome
$endgroup$
– pythinker
Apr 25 at 9:19
add a comment |
$begingroup$
I recommend using word2vec as feature vector of words and LSTM autoencoder to encode a sentence (or text) . After you get a vector for each sentence (or text), you can cluster your sentences (or texts) using a variety of clustering techniques like k-means or dbscan and represent them using t-sne or u-map. Start from here:
https://blog.myyellowroad.com/unsupervised-sentence-representation-with-deep-learning-104b90079a93
answered Apr 9 at 17:06
pythinkerpythinker
8641314
$endgroup$
$begingroup$
thank you for your answer ! regarding the sentence encoding, there is an existing implementation of the TF-IDF algorithm in sklearn, here is the tutorial (pythonprogramminglanguage.com/kmeans-text-clustering). Thus, i am wondering why it's recommended to use encoding techniques such as word2vec and LSTM. Can you please explain the advantages of using such techniques compared to the one implemented in sklearn for instance?
$endgroup$
– Takwa
Apr 19 at 14:12
$begingroup$
You’re welcome. Actually, the first advantage of using word2vec over tf-idf is that, word2vec contains contextual information but tf-idf does not. The second advantage is that, word2vec uses information from a large dataset (pre-training), so it better models the language than tf-idf. And for the third advantage you should consider that as the vocabulary size increases, the tf-idf size increases, too. However, pre-trained word2vec vectors have fixed size, regardless of vocabulary size.
$endgroup$
– pythinker
Apr 19 at 18:45
$begingroup$
Thank you for the explanation @pythinker !
$endgroup$
– Takwa
Apr 25 at 8:21
$begingroup$
@Takwa You’re welcome
$endgroup$
– pythinker
Apr 25 at 9:19
add a comment |
$begingroup$
I recommend using word2vec as feature vector of words and LSTM autoencoder to encode a sentence (or text) . After you get a vector for each sentence (or text), you can cluster your sentences (or texts) using a variety of clustering techniques like k-means or dbscan and represent them using t-sne or u-map. Start from here:
https://blog.myyellowroad.com/unsupervised-sentence-representation-with-deep-learning-104b90079a93
answered Apr 9 at 17:06
pythinkerpythinker
8641314
$endgroup$
I recommend using word2vec as feature vector of words and LSTM autoencoder to encode a sentence (or text) . After you get a vector for each sentence (or text), you can cluster your sentences (or texts) using a variety of clustering techniques like k-means or dbscan and represent them using t-sne or u-map. Start from here:
https://blog.myyellowroad.com/unsupervised-sentence-representation-with-deep-learning-104b90079a93
answered Apr 9 at 17:06
pythinkerpythinker
8641314
answered Apr 9 at 17:06
pythinkerpythinker
8641314
answered Apr 9 at 17:06
pythinkerpythinker
8641314
answered Apr 9 at 17:06
pythinkerpythinker
8641314
8641314
$begingroup$
thank you for your answer ! regarding the sentence encoding, there is an existing implementation of the TF-IDF algorithm in sklearn, here is the tutorial (pythonprogramminglanguage.com/kmeans-text-clustering). Thus, i am wondering why it's recommended to use encoding techniques such as word2vec and LSTM. Can you please explain the advantages of using such techniques compared to the one implemented in sklearn for instance?
$endgroup$
– Takwa
Apr 19 at 14:12
$begingroup$
You’re welcome. Actually, the first advantage of using word2vec over tf-idf is that, word2vec contains contextual information but tf-idf does not. The second advantage is that, word2vec uses information from a large dataset (pre-training), so it better models the language than tf-idf. And for the third advantage you should consider that as the vocabulary size increases, the tf-idf size increases, too. However, pre-trained word2vec vectors have fixed size, regardless of vocabulary size.
$endgroup$
– pythinker
Apr 19 at 18:45
$begingroup$
Thank you for the explanation @pythinker !
$endgroup$
– Takwa
Apr 25 at 8:21
$begingroup$
@Takwa You’re welcome
$endgroup$
– pythinker
Apr 25 at 9:19
add a comment |
$begingroup$
thank you for your answer ! regarding the sentence encoding, there is an existing implementation of the TF-IDF algorithm in sklearn, here is the tutorial (pythonprogramminglanguage.com/kmeans-text-clustering). Thus, i am wondering why it's recommended to use encoding techniques such as word2vec and LSTM. Can you please explain the advantages of using such techniques compared to the one implemented in sklearn for instance?
$endgroup$
– Takwa
Apr 19 at 14:12
$begingroup$
You’re welcome. Actually, the first advantage of using word2vec over tf-idf is that, word2vec contains contextual information but tf-idf does not. The second advantage is that, word2vec uses information from a large dataset (pre-training), so it better models the language than tf-idf. And for the third advantage you should consider that as the vocabulary size increases, the tf-idf size increases, too. However, pre-trained word2vec vectors have fixed size, regardless of vocabulary size.
$endgroup$
– pythinker
Apr 19 at 18:45
$begingroup$
Thank you for the explanation @pythinker !
$endgroup$
– Takwa
Apr 25 at 8:21
$begingroup$
@Takwa You’re welcome
$endgroup$
– pythinker
Apr 25 at 9:19
$begingroup$
thank you for your answer ! regarding the sentence encoding, there is an existing implementation of the TF-IDF algorithm in sklearn, here is the tutorial (pythonprogramminglanguage.com/kmeans-text-clustering). Thus, i am wondering why it's recommended to use encoding techniques such as word2vec and LSTM. Can you please explain the advantages of using such techniques compared to the one implemented in sklearn for instance?
$endgroup$
– Takwa
Apr 19 at 14:12
$begingroup$
thank you for your answer ! regarding the sentence encoding, there is an existing implementation of the TF-IDF algorithm in sklearn, here is the tutorial (pythonprogramminglanguage.com/kmeans-text-clustering). Thus, i am wondering why it's recommended to use encoding techniques such as word2vec and LSTM. Can you please explain the advantages of using such techniques compared to the one implemented in sklearn for instance?
$endgroup$
– Takwa
Apr 19 at 14:12
$begingroup$
You’re welcome. Actually, the first advantage of using word2vec over tf-idf is that, word2vec contains contextual information but tf-idf does not. The second advantage is that, word2vec uses information from a large dataset (pre-training), so it better models the language than tf-idf. And for the third advantage you should consider that as the vocabulary size increases, the tf-idf size increases, too. However, pre-trained word2vec vectors have fixed size, regardless of vocabulary size.
$endgroup$
– pythinker
Apr 19 at 18:45
$begingroup$
You’re welcome. Actually, the first advantage of using word2vec over tf-idf is that, word2vec contains contextual information but tf-idf does not. The second advantage is that, word2vec uses information from a large dataset (pre-training), so it better models the language than tf-idf. And for the third advantage you should consider that as the vocabulary size increases, the tf-idf size increases, too. However, pre-trained word2vec vectors have fixed size, regardless of vocabulary size.
$endgroup$
– pythinker
Apr 19 at 18:45
$begingroup$
Thank you for the explanation @pythinker !
$endgroup$
– Takwa
Apr 25 at 8:21
$begingroup$
Thank you for the explanation @pythinker !
$endgroup$
– Takwa
Apr 25 at 8:21
$begingroup$
@Takwa You’re welcome
$endgroup$
– pythinker
Apr 25 at 9:19
$begingroup$
@Takwa You’re welcome
$endgroup$
– pythinker
Apr 25 at 9:19
add a comment |
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