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Show HN: Neural network that impersonates writers

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Re: Show HN: Neural network that impersonates writers

#11

Post by strong_ai »

this doesn't look like a neural net to me. from NeuralNetwork.py

  from sklearn.neighbors import KNeighborsClassifier
  # Create a sperate neural network for each identifier
  for index in range(0, len(NaturalLanguageObject._Identifiers)):
       nn = KNeighborsClassifier()
       self._Networks.append(nn)

Re: Show HN: Neural network that impersonates writers

#12

Post by w_t_payne »

I'd be interested to know if this could be turned into a tool that lets you know how well your writing (or coding) matches the "house style". (Mostly for technical documentation, requirements specs etc...)

I'd be even more interested if it could be turned into a sublime text plugin that highlights words / phrases that deviate most strongly from the house style.

Re: Show HN: Neural network that impersonates writers

#13

Post by nl »

strong_ai wrote: post #11

this doesn't look like a neural net to me. from NeuralNetwork.py from sklearn.neighbors import KNeighborsClassifier # Create a sperate neural network for each identifier for index in range(0, len(NaturalLanguageObject._Identifiers)): nn = KNeighborsClassifier() self._Networks.append(nn)

I've glanced at the code now. You are absolutely correct.

Wow.

There's some good reasons to think this approach won't work at all. If I understand it correctly I think it is attempting to predict part of speech using previously observed values.

That's an interesting idea, and might be somewhat valuable as a feature to use in a text generator, but on its own won't be enough to ever generate sentences that make sense (because some specific sequences just don't make sense).

Re: Show HN: Neural network that impersonates writers

#14

Post by ching_wow_ka »

strong_ai wrote: post #11

this doesn't look like a neural net to me. from NeuralNetwork.py from sklearn.neighbors import KNeighborsClassifier # Create a sperate neural network for each identifier for index in range(0, len(NaturalLanguageObject._Identifiers)): nn = KNeighborsClassifier() self._Networks.append(nn)

So then it seems like the author of the code doesn't understand that "NN" means "Nearest Neighbors" and not "Neural Network"?

He mentions that he used sklearn's Neural Network libraries in his blog post, but sklearn doesn't have any aside from RBM.

Re: Show HN: Neural network that impersonates writers

#15

Post by kylebgorman »

frisco wrote: post #2

Fun hack. If anything, it highlights how compelling deep learning and RNNs are: no messing with NLP, no messing with building other features or adding up classifiers, etc. The manual feature engineering means it might work better on a smaller dataset, but even then probably not. For comparison with Andrej Karpathy's RNN code ( http://karpathy.github.io/2015/05/21/rnn-effectiveness/ ) training on the "HarryPotter(xxla…

I just can't agree that a simple, linear-time operation like "tokenizing words and basic n-gram models out of them" is a tedious problem like you seem to be implying, nor do I feel a solution to this very-solved problem is "compelling". Word tokenization and n-gram models are simple, unreasonably effective, and very fast. If character-based RNNs do better (albeit far more slowly during training), great, but nothing to see here, let's move along.

As I've posted here before, people have been training character n-gram models and getting language modeling performances comparable to those from word-based models---without using neural networks---for at least a decade. That it works with RNNs is no surprise because it worked just fine with the much more constrained predecessor technology.

Re: Show HN: Neural network that impersonates writers

#17

Post by nl »

ching_wow_ka wrote: post #14
strong_ai wrote: post #11

this doesn't look like a neural net to me. from NeuralNetwork.py from sklearn.neighbors import KNeighborsClassifier # Create a sperate neural network for each identifier for index in range(0, len(NaturalLanguageObject._Identifiers)): nn = KNeighborsClassifier() self._Networks.append(nn)

So then it seems like the author of the code doesn't understand that "NN" means "Nearest Neighbors" and not "Neural Network"? He mentions that he used sklearn's Neural Network libraries in his blog post, but sklearn doesn't have any aside from RBM.

It is almost the perfect example of the "Danger Zone" in http://drewconway.com/zia/2013/3/26/the-data-science-venn-di...

Re: Show HN: Neural network that impersonates writers

#18

Post by lawlessone »

ching_wow_ka wrote: post #14
strong_ai wrote: post #11

this doesn't look like a neural net to me. from NeuralNetwork.py from sklearn.neighbors import KNeighborsClassifier # Create a sperate neural network for each identifier for index in range(0, len(NaturalLanguageObject._Identifiers)): nn = KNeighborsClassifier() self._Networks.append(nn)

So then it seems like the author of the code doesn't understand that "NN" means "Nearest Neighbors" and not "Neural Network"? He mentions that he used sklearn's Neural Network libraries in his blog post, but sklearn doesn't have any aside from RBM.

Should i be impressed that even though the author was not using the tool they thought then still got it to spit out something?

it must be a robust tool.

Re: Show HN: Neural network that impersonates writers

#19

Post by [deleted] »

lawlessone wrote: post #18
ching_wow_ka wrote: post #14

Earlier quoted context omitted.

So then it seems like the author of the code doesn't understand that "NN" means "Nearest Neighbors" and not "Neural Network"? He mentions that he used sklearn's Neural Network libraries in his blog post, but sklearn doesn't have any aside from RBM.

Should i be impressed that even though the author was not using the tool they thought then still got it to spit out something? it must be a robust tool.

[deleted]

Re: Show HN: Neural network that impersonates writers

#20

Post by throwaway7767 »

[deleted] wrote: post #16

[deleted]

Do you have a link to a good dataset of english-language NK propaganda? I've been playing around with char-rnn and that sounds like a good one to play with.

I agree the output here is lower quality than I would expect from an RNN, and quite similar to what I would expect from ngram-based markov bots.

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