Python NLTK Bayesian Classifier for word sense disambiguation - 92% accuracy
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Re: Python NLTK Bayesian Classifier for word sense disambiguation - 92% accuracy
#2As someone who has spent a considerable amount of time studying NLP, I have to say that this post outlines a pretty naive approach when it comes to disambiguating words.
Here are some questions:
- What happens when we change the language model? - What happens when we intersperse language models (English phrases within Chinese)? - What if someone were to just say "i love apple"?
This post title is also very misleading. The 92% accuracy reflects only one particular use case. How about attempting to disambiguate hundreds and thousands of terms?
Re: Python NLTK Bayesian Classifier for word sense disambiguation - 92% accuracy
#3A blinking favicon? Seriously? I couldn't finish the article because my eyes jumped to the tab bar every 5 seconds.
Has anyone else seen this elsewhere? It's new to me and I was surprised by how obnoxious it was given that the web isn't exactly a stranger to obnoxious flashing content.
Re: Python NLTK Bayesian Classifier for word sense disambiguation - 92% accuracy
#4This is an interesting exercise in building a very specific word disambiguator ('apple' the company vs 'apple' the fruit).
It is a testmanet to NLTK that this can be accomplished in less than 100 lines.
Re: Python NLTK Bayesian Classifier for word sense disambiguation - 92% accuracy
#592% accuracy, unfortunately, isn't good enough.
Bag-of-words models perform pretty well at classification and search, and the main thing you need to improve search is to boost scores when words are close together.
You might think you could improve performance by using semantically better defined features, but even 92% accuracy adds enough noise to foil your plans.
It's a big problem in A.I. systems that have multiple stages. You might have 5 steps in a chain which are each 90% accurate, but put them together and you've got a system that sucks. Ultimately there's a need for a holistic approach that can use higher-level information to fix mistakes and ambiguities at the lower levels.
Re: Python NLTK Bayesian Classifier for word sense disambiguation - 92% accuracy
#6A blinking favicon? Seriously? I couldn't finish the article because my eyes jumped to the tab bar every 5 seconds. Has anyone else seen this elsewhere? It's new to me and I was surprised by how obnoxious it was given that the web isn't exactly a stranger to obnoxious flashing content.
It doesn't blink for me. (In case you're wondering why you're getting downvoted -- though I didn't downvote you myself.)
Re: Python NLTK Bayesian Classifier for word sense disambiguation - 92% accuracy
#7I always prefer to see these things in context (how well does a naive rule-based classifier do? what's the P/R/F-score i.e. how many Apples are apples and apples are Apples? What about Apple Records?)
It's still fun to remember how quick and easy something like this is though. Any interest in similar articles on named entity recognition, sentiment/topic classification and spam filtering? I've been meaning to do a few for a while, but you know how it is.
Re: Python NLTK Bayesian Classifier for word sense disambiguation - 92% accuracy
#8As someone who has spent a considerable amount of time studying NLP, I have to say that this post outlines a pretty naive approach when it comes to disambiguating words. Here are some questions: - What happens when we change the language model? - What happens when we intersperse language models (English phrases within Chinese)? - What if someone were to just say "i love apple"? This post title is also very misleading…
> As someone who has spent a considerable amount of time studying NLP
I'm quite interested in how will you approach this problem ?
Re: Python NLTK Bayesian Classifier for word sense disambiguation - 92% accuracy
#992% accuracy, unfortunately, isn't good enough. Bag-of-words models perform pretty well at classification and search, and the main thing you need to improve search is to boost scores when words are close together. You might think you could improve performance by using semantically better defined features, but even 92% accuracy adds enough noise to foil your plans. It's a big problem in A.I. systems that have multiple…
To be fair, he did this in a couple of hours.
Re: Python NLTK Bayesian Classifier for word sense disambiguation - 92% accuracy
#1092% accuracy, unfortunately, isn't good enough. Bag-of-words models perform pretty well at classification and search, and the main thing you need to improve search is to boost scores when words are close together. You might think you could improve performance by using semantically better defined features, but even 92% accuracy adds enough noise to foil your plans. It's a big problem in A.I. systems that have multiple…
To be fair, he did this in a couple of hours.
Sure, but the fact is that nobody actually needs a "word-sense disambiguator", they need a search system with better accuracy, or a classifier with better accuracy or an information extraction system that turns text into facts.
Many areas in NLP are like this. You can get 92% accuracy in a few hours of work, and then you can get 93% after a week or work, and then you can write a whole PhD thesis about how you got 94% accuracy.
To a certain extent, there are approaches, such as the Support Vector Machine that are "unreasonably effective" but once you get past that, you often have to confront issues that everybody wants to sweep under the rug to make a real breakthrough.
For instance, there was that NELL paper that came out a few months ago; NELL extracted facts from text but it had no idea that "Barack Obama is the President of the United States" was true in 2010, and that "Richard Nixon is the President of the United States" was true in 1972. If you can't handle the fact that different people believe different things and that statements have expiration dates, no wonder you can only get 70% accuracy in IX