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Ethan Yun
Ethan Yun
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The topic of the conversation should be expressed in one word within the conversation. So instead of making your model guess from a bunch of different combinations of chars, try instead to train your model to guess what word within the conversation is the topic. You probably shouldn't plug in the P1, P2, ... thing because the person speaking is not relevant.

This problem doesn't have to be solved using ML because there is already a traditional solution. Check this link: https://github.com/bnosac/textrank/blob/master/vignettes/textrank.Rmdhttps://github.com/vgrabovets/multi_rake

The topic of the conversation should be expressed in one word within the conversation. So instead of making your model guess from a bunch of different combinations of chars, try instead to train your model to guess what word within the conversation is the topic. You probably shouldn't plug in the P1, P2, ... thing because the person speaking is not relevant.

This problem doesn't have to be solved using ML because there is already a traditional solution. Check this link: https://github.com/bnosac/textrank/blob/master/vignettes/textrank.Rmd

The topic of the conversation should be expressed in one word within the conversation. So instead of making your model guess from a bunch of different combinations of chars, try instead to train your model to guess what word within the conversation is the topic. You probably shouldn't plug in the P1, P2, ... thing because the person speaking is not relevant.

This problem doesn't have to be solved using ML because there is already a traditional solution. Check this link: https://github.com/vgrabovets/multi_rake

There is already a traditional approach to this problem.
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Ethan Yun
Ethan Yun
  • 329
  • 1
  • 8

The topic of the conversation should be expressed in one word within the conversation. So instead of making your model guess from a bunch of different combinations of chars, try instead to train your model to guess what word within the conversation is the topic. You probably shouldn't plug in the P1, P2, ... thing because the person speaking is not relevant.

As a supervised learningThis problem, you could give a model (neural network for example, doesn't have to be solved using ML because there are a lot of those used in problems like these) a bunch of conversations and the topic. Make sure that the topic is already a word in the conversation because we don't want ambiguous words floating around as labels. I am not quite sure how to measure error for backpropagation, etc. But I'm sure you can figure it out with more researchtraditional solution. Check this link: https://github.com/bnosac/textrank/blob/master/vignettes/textrank.Rmd

The topic of the conversation should be expressed in one word within the conversation. So instead of making your model guess from a bunch of different combinations of chars, try instead to train your model to guess what word within the conversation is the topic. You probably shouldn't plug in the P1, P2, ... thing because the person speaking is not relevant.

As a supervised learning problem, you could give a model (neural network for example, there are a lot of those used in problems like these) a bunch of conversations and the topic. Make sure that the topic is a word in the conversation because we don't want ambiguous words floating around as labels. I am not quite sure how to measure error for backpropagation, etc. But I'm sure you can figure it out with more research.

The topic of the conversation should be expressed in one word within the conversation. So instead of making your model guess from a bunch of different combinations of chars, try instead to train your model to guess what word within the conversation is the topic. You probably shouldn't plug in the P1, P2, ... thing because the person speaking is not relevant.

This problem doesn't have to be solved using ML because there is already a traditional solution. Check this link: https://github.com/bnosac/textrank/blob/master/vignettes/textrank.Rmd

Source Link
Ethan Yun
Ethan Yun
  • 329
  • 1
  • 8

The topic of the conversation should be expressed in one word within the conversation. So instead of making your model guess from a bunch of different combinations of chars, try instead to train your model to guess what word within the conversation is the topic. You probably shouldn't plug in the P1, P2, ... thing because the person speaking is not relevant.

As a supervised learning problem, you could give a model (neural network for example, there are a lot of those used in problems like these) a bunch of conversations and the topic. Make sure that the topic is a word in the conversation because we don't want ambiguous words floating around as labels. I am not quite sure how to measure error for backpropagation, etc. But I'm sure you can figure it out with more research.