Contents
What is the purpose of the embedding dimension in NLP?
An embedding is a relatively low-dimensional space into which you can translate high-dimensional vectors. Embeddings make it easier to do machine learning on large inputs like sparse vectors representing words.
What is the best embedding dimension?
A good rule of thumb is 4th root of the number of categories. For text classification, this is the 4th root of your vocabulary length. Typical nnlm models on google hub have the embedding size of 128.
What is word embedding size?
output_dim: This is the size of the vector space in which words will be embedded. It defines the size of the output vectors from this layer for each word. For example, it could be 32 or 100 or even larger. Test different values for your problem.
What kind of vector space does word2vec use?
Word2Vec produces a vector space, typically of several hundred dimensions, with each unique word in the corpus such that words that share common contexts in the corpus are located close to one another in the space.
How many words can be captured in a vectorizer?
I am going to use the Tf-Idf vectorizer with a limit of 10,000 words (so the length of my vocabulary will be 10k), capturing unigrams (i.e. “ new ” and “ york ”) and bigrams (i.e. “ new york ”). I will provide the code for the classic count vectorizer as well:
How to visualize a vector of a word?
I used Tensorboard to visualize the embeddings. Tensorboard allows you to see the whole word cloud by using PCA to select 3 main axis to project the data. Super cool! You can type in any word and it will show its neighbours. You can also isolate the 101 points closest to it. See clip below. You can find the full code on my Github repo.
Which is the best model for generating word vectors?
There are two main models for generating these word vectors — Continuous Bag of Words (CBOW) and Skip Gram Model. The CBOW model tries to predict the center word given context word while skip gram model tries to predict context words given center word. A simplified example would be: CBOW: The cat ate _____.