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How to predict sentences using numeric features in a DL model?

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0

$begingroup$

I know of Deep Learning and ML models which takes numeric features as inputs, and are able to predict or classify the target, which works well when doing things like binary or multi class classification, or regression problems.

However, I wonder if there is a way to use the numeric features as predictors for a ML/DL model, and predict the sentences (by learning from historical data of the same), such ad by using an LSTM, but am not sure of if there is a possible minimal example which can better clarify this aspect. So basically, the aim is that given various features (all numeric values), to predict the sentence correspondig to these as a target. The model should learn from historical features which have the corresponding sentence target (a collection of words in English language) to achieve this purpose. Any help and minimal example in this regard is highly appreciated.

deep-learning nlp bigdata text-generation

share|improve this question

asked 14 mins ago

JChatJChat

1378

$endgroup$

add a comment | 

0

$begingroup$

I know of Deep Learning and ML models which takes numeric features as inputs, and are able to predict or classify the target, which works well when doing things like binary or multi class classification, or regression problems.

However, I wonder if there is a way to use the numeric features as predictors for a ML/DL model, and predict the sentences (by learning from historical data of the same), such ad by using an LSTM, but am not sure of if there is a possible minimal example which can better clarify this aspect. So basically, the aim is that given various features (all numeric values), to predict the sentence correspondig to these as a target. The model should learn from historical features which have the corresponding sentence target (a collection of words in English language) to achieve this purpose. Any help and minimal example in this regard is highly appreciated.

deep-learning nlp bigdata text-generation

share|improve this question

asked 14 mins ago

JChatJChat

1378

$endgroup$

add a comment | 

$begingroup$

I know of Deep Learning and ML models which takes numeric features as inputs, and are able to predict or classify the target, which works well when doing things like binary or multi class classification, or regression problems.

However, I wonder if there is a way to use the numeric features as predictors for a ML/DL model, and predict the sentences (by learning from historical data of the same), such ad by using an LSTM, but am not sure of if there is a possible minimal example which can better clarify this aspect. So basically, the aim is that given various features (all numeric values), to predict the sentence correspondig to these as a target. The model should learn from historical features which have the corresponding sentence target (a collection of words in English language) to achieve this purpose. Any help and minimal example in this regard is highly appreciated.

deep-learning nlp bigdata text-generation

share|improve this question

asked 14 mins ago

JChatJChat

1378

$endgroup$

share|improve this question

asked 14 mins ago

JChatJChat

1378

share|improve this question

asked 14 mins ago

JChatJChat

1378

asked 14 mins ago

JChatJChat

1378

asked 14 mins ago

JChatJChat

1378