Word2vec is a open source tool to calculate the words distance provided by Google. It can be used by inputting a word and output the ranked word lists according to the similarity.
The idea of word2vec is to maximise the similarity (dot product) between the vectors for words which appear close together (in the context of each other) in text, and minimise the similarity of words that do not.
Word2vec & related algorithms are very data-hungry: all of their beneficial qualities arise from the tug-of-war between many varied usage examples for the same word. So if you have a toy-sized dataset, you won't get a set of vectors with useful interrelationships. But also, rare words in your larger dataset won't get good vectors.
I want to create a text file that is essentially a dictionary, with each word being paired with its vector representation through word2vec. I'm assuming the process would be to first train word2vec...
It is possible, but not from word2vec. The composition of word vectors in order to obtain higher-level representations for sentences (and further for paragraphs and documents) is a really active research topic.
import gensim # Load pre-trained Word2Vec model. model = gensim.models.Word2Vec.load("modelName.model") now you can train the model as usual. also, if you want to be able to save it and retrain it multiple times, here's what you should do
from gensim.models import Word2Vec from nltk.tokenize import word_tokenize import nltk nltk.download('punkt') # Sample sentences sentences = [ "This is a sample sentence.", "Word embeddings are cool.", "I love natural language processing." ] # Tokenize the sentences tokenized_sentences = [word_tokenize(sentence.lower()) for sentence in sentences] # Train the Word2Vec model model = Word2Vec ...
