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This any useful for sentiment analysis and plagiarism detection? I might give it a go after I'm done with my current projects

ovi256 10y ago
This will enable writing a plagiarism detector which will not be fooled by the simple strategy of replacing words with their synonyms. Given that synonyms have very similar embeddings, you can compute a distance between two phrases by computing the distance between their word embeddings. And that's just what comes to mind right now. For sentiment detection, I could see a similar experiment to [1] working, but instead of discriminating between newsgroups, you classify sentiment. [1] https://blog.keras.io/using-pre-trained-word-embeddings-in-a-keras-model.html https://blog.keras.io/using-pre-trained-word-embeddings-in-a...
- webmaven 10y ago
  > This will enable writing a plagiarism detector which will not be fooled by the simple strategy of replacing words with their synonyms. I'm not sure how helpful that will be, as you may end up with a system that detects whenever a student expresses similar thoughts (and lets face it, the educational system is all about getting students to conform to conventional patterns of thinking) in their own words. And if the system doesn't detect re-expression of the same ideas, then a system that automatically rewrites essays in a slightly different style (essentially, an English-to-English neural machine translation) will defeat it. The endgame would be grading student essays on how well they express an entirely original idea, which is an unreasonable standard.
Jack000 10y ago
I think this would be difficult because a lot of school reports already come down to re-formatting textbook ideas in your own words. Could be a tough decision of how different it needs to be to be considered original work. might be a lot easier to make a plagiarism generator instead - keep the overall meaning of sentences but use synonyms or deliberately off-meaning words.

ovi256 10y ago

This will enable writing a plagiarism detector which will not be fooled by the simple strategy of replacing words with their synonyms. Given that synonyms have very similar embeddings, you can compute a distance between two phrases by computing the distance between their word embeddings. And that's just what comes to mind right now. For sentiment detection, I could see a similar experiment to [1] working, but instead of discriminating between newsgroups, you classify sentiment. [1] https://blog.keras.io/using-pre-trained-word-embeddings-in-a-keras-model.html https://blog.keras.io/using-pre-trained-word-embeddings-in-a...

webmaven 10y ago
> This will enable writing a plagiarism detector which will not be fooled by the simple strategy of replacing words with their synonyms. I'm not sure how helpful that will be, as you may end up with a system that detects whenever a student expresses similar thoughts (and lets face it, the educational system is all about getting students to conform to conventional patterns of thinking) in their own words. And if the system doesn't detect re-expression of the same ideas, then a system that automatically rewrites essays in a slightly different style (essentially, an English-to-English neural machine translation) will defeat it. The endgame would be grading student essays on how well they express an entirely original idea, which is an unreasonable standard.

Jack000 10y ago

I think this would be difficult because a lot of school reports already come down to re-formatting textbook ideas in your own words. Could be a tough decision of how different it needs to be to be considered original work. might be a lot easier to make a plagiarism generator instead - keep the overall meaning of sentences but use synonyms or deliberately off-meaning words.