Exploring Word2Vec
sujayskumar.blogspot.com
Exploring Word2Vec
1–10 of 18 posts
Re: Exploring Word2Vec
#2We recently open sourced a library that makes it easy to quickly get started with pre-trained word vector models like word2vec from Google, Facebook, and Stanford. It adds a ton of extra functionality like fast similiarity indexing with Annoy and fairly robust out-of-vocabulary word lookups (handling misspellings) out of the box. If you want to quickly get started with pre-built models, it might be worth checking out along with Gensim:
Re: Exploring Word2Vec
#3> One important thing to note is that Word2Vec does not consider the positional variable of the context words.
There is a very interesting new approach of learning Gaussian mixtures from words, which addresses this problem: https://github.com/benathi/word2gm
It also solves another interesting problem, which is that Word2Vec doesn't always put "categories" close to their actual contents.
Eg. I wrote a simple AI for playing Codenames with word vectors: https://github.com/thomasahle/codenames But where a human use the clue "Countries 2" for "Germany" and "England", the Glove vectors (at least in my implementation) much prefers the clue "France 2", which is pretty confusing at first.
Re: Exploring Word2Vec
#4We recently open sourced a library that makes it easy to quickly get started with pre-trained word vector models like word2vec from Google, Facebook, and Stanford. It adds a ton of extra functionality like fast similiarity indexing with Annoy and fairly robust out-of-vocabulary word lookups (handling misspellings) out of the box. If you want to quickly get started with pre-built models, it might be worth checking out…
This is great, the examples in the README are very interesting.
Re: Exploring Word2Vec
#5We recently open sourced a library that makes it easy to quickly get started with pre-trained word vector models like word2vec from Google, Facebook, and Stanford. It adds a ton of extra functionality like fast similiarity indexing with Annoy and fairly robust out-of-vocabulary word lookups (handling misspellings) out of the box. If you want to quickly get started with pre-built models, it might be worth checking out…
Wow. Good job!
Some better examples would really finish this off!
Re: Exploring Word2Vec
#6> One important thing to note is that Word2Vec does not consider the positional variable of the context words. There is a very interesting new approach of learning Gaussian mixtures from words, which addresses this problem: https://github.com/benathi/word2gm It also solves another interesting problem, which is that Word2Vec doesn't always put "categories" close to their actual contents. Eg. I wrote a simple AI for pl…
The Gaussian mixture representation is quite interesting, so thanks for the link. But it seems like they do not make use of the relative position of words in the text either. They simply maximize the margin of the expected likelihood for words that occur in the same context over those that do not.
However, in most cases it's probably actually better to ignore word order, since that captures more semantic relatedness rather than the syntactic features that influence word order. It also lets you translate between languages that do not necessarily have similar grammar: "Word Translation Without Parallel Data" https://arxiv.org/abs/1710.04087
Re: Exploring Word2Vec
#7Can someone explain why isn't the word 'the' considered part of the vocabulary in the blog post?
Re: Exploring Word2Vec
#8Can someone explain why isn't the word 'the' considered part of the vocabulary in the blog post?
usual procedure in NLP: https://en.wikipedia.org/wiki/Stop_words
Re: Exploring Word2Vec
#9I enjoyed this writeup, as well as the canonical word2vec explanation linked.
I will say that he’s wrong that NFL never co-occurs with ML. I’ve had discussions involving the No Free Lunch theorem by its initials.
Re: Exploring Word2Vec
#10> One important thing to note is that Word2Vec does not consider the positional variable of the context words. There is a very interesting new approach of learning Gaussian mixtures from words, which addresses this problem: https://github.com/benathi/word2gm It also solves another interesting problem, which is that Word2Vec doesn't always put "categories" close to their actual contents. Eg. I wrote a simple AI for pl…
The Gaussian mixture representation is quite interesting, so thanks for the link. But it seems like they do not make use of the relative position of words in the text either. They simply maximize the margin of the expected likelihood for words that occur in the same context over those that do not. However, in most cases it's probably actually better to ignore word order, since that captures more semantic relatedness…
Whether it's okay to ignore word order really depends on your goals. In my work, tracking word order to build a sentence representation is necessary to get composition rules correct. You can get away with ignoring order surprisingly often but when it's needed, it's vital. When aggregated across an entire corpus, order ends up mattering a lot, in an absolute sense (something looking only at accuracy numbers will hide).
Compare the following:
1) You must state the benefit.
2) You must benefit the state.
3) Feed fish vs. Fish feed.
Sometimes limits in inference:
1) A loves B.
2) Cats eat fish.
3) Cats are mammals.
Sometimes the sequence preceding or following will be important:
1) Problems with learning
2) Learning with problems
3) It has a learning problem
4) A learning problem for it.
Or consider an example from:
https://www.researchgate.net/publication/2335962_How_Well_Ca...
1) It was not the sales manager who hit the bottle that day, but the office worker with the serious drinking problem.
2) That day the office manager, who was drinking, hit the problem sales worker with the bottle, but it was not serious.
Order ends up mattering when you want to capture nuance or power higher order inference.