Sentimentron / Word-Embeddings

Quick word-embeddings demonstration using r9y9/nnet

Quick word-embeddings demonstration using r9y9/nnet.

News article from WikiNews is under a Creative Commons Attribution 2.5 License.

Does it work? Kind of. On the news article, the closest word representations (by Euclidean distance) are television, thickness, and complaints, which - given that the article's about people complaining about the size of a burger featured in a television advert - isn't totally wrong. Your mileage might varybecause it seems quite sensitive to the initialisation of the initial weight-embedding matrix.

This also requires a patched version of nnet which you can find here.

Todo list:

• Add support for custom loss functions in mlp
• Use this as an opportunity to profile and improve performance in mlp
• Tighten up the code for integration into golearn.
• For some reason, I can't use the LayerFuncs defined in nnet, so that's something to fix.

License

Copyright (c) 2014, Richard Townsend. All rights reserved.

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