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.. Copyright (C) 2001-2019 NLTK Project
.. For license information, see LICENSE.TXT
=======================================
Demonstrate word embedding using Gensim
=======================================
We demonstrate three functions:
- Train the word embeddings using brown corpus;
- Load the pre-trained model and perform simple tasks; and
- Pruning the pre-trained binary model.
>>> import gensim
---------------
Train the model
---------------
Here we train a word embedding using the Brown Corpus:
>>> from nltk.corpus import brown
>>> model = gensim.models.Word2Vec(brown.sents())
It might take some time to train the model. So, after it is trained, it can be saved as follows:
>>> model.save('brown.embedding')
>>> new_model = gensim.models.Word2Vec.load('brown.embedding')
The model will be the list of words with their embedding. We can easily get the vector representation of a word.
>>> len(new_model['university'])
100
There are some supporting functions already implemented in Gensim to manipulate with word embeddings.
For example, to compute the cosine similarity between 2 words:
>>> new_model.similarity('university','school') > 0.3
True
---------------------------
Using the pre-trained model
---------------------------
NLTK includes a pre-trained model which is part of a model that is trained on 100 billion words from the Google News Dataset.
The full model is from https://code.google.com/p/word2vec/ (about 3 GB).
>>> from nltk.data import find
>>> word2vec_sample = str(find('models/word2vec_sample/pruned.word2vec.txt'))
>>> model = gensim.models.KeyedVectors.load_word2vec_format(word2vec_sample, binary=False)
We pruned the model to only include the most common words (~44k words).
>>> len(model.vocab)
43981
Each word is represented in the space of 300 dimensions:
>>> len(model['university'])
300
Finding the top n words that are similar to a target word is simple. The result is the list of n words with the score.
>>> model.most_similar(positive=['university'], topn = 3)
[(u'universities', 0.70039...), (u'faculty', 0.67809...), (u'undergraduate', 0.65870...)]
Finding a word that is not in a list is also supported, although, implementing this by yourself is simple.
>>> model.doesnt_match('breakfast cereal dinner lunch'.split())
'cereal'
Mikolov et al. (2013) figured out that word embedding captures much of syntactic and semantic regularities. For example,
the vector 'King - Man + Woman' is close to 'Queen' and 'Germany - Berlin + Paris' is close to 'France'.
>>> model.most_similar(positive=['woman','king'], negative=['man'], topn = 1)
[(u'queen', 0.71181...)]
>>> model.most_similar(positive=['Paris','Germany'], negative=['Berlin'], topn = 1)
[(u'France', 0.78840...)]
We can visualize the word embeddings using t-SNE (http://lvdmaaten.github.io/tsne/). For this demonstration, we visualize the first 1000 words.
| import numpy as np
| labels = []
| count = 0
| max_count = 1000
| X = np.zeros(shape=(max_count,len(model['university'])))
|
| for term in model.vocab:
| X[count] = model[term]
| labels.append(term)
| count+= 1
| if count >= max_count: break
|
| # It is recommended to use PCA first to reduce to ~50 dimensions
| from sklearn.decomposition import PCA
| pca = PCA(n_components=50)
| X_50 = pca.fit_transform(X)
|
| # Using TSNE to further reduce to 2 dimensions
| from sklearn.manifold import TSNE
| model_tsne = TSNE(n_components=2, random_state=0)
| Y = model_tsne.fit_transform(X_50)
|
| # Show the scatter plot
| import matplotlib.pyplot as plt
| plt.scatter(Y[:,0], Y[:,1], 20)
|
| # Add labels
| for label, x, y in zip(labels, Y[:, 0], Y[:, 1]):
| plt.annotate(label, xy = (x,y), xytext = (0, 0), textcoords = 'offset points', size = 10)
|
| plt.show()
------------------------------
Prune the trained binary model
------------------------------
Here is the supporting code to extract part of the binary model (GoogleNews-vectors-negative300.bin.gz) from https://code.google.com/p/word2vec/
We use this code to get the `word2vec_sample` model.
| import gensim
| from gensim.models.word2vec import Word2Vec
| # Load the binary model
| model = Word2Vec.load_word2vec_format('GoogleNews-vectors-negative300.bin.gz', binary = True);
|
| # Only output word that appear in the Brown corpus
| from nltk.corpus import brown
| words = set(brown.words())
| print (len(words))
|
| # Output presented word to a temporary file
| out_file = 'pruned.word2vec.txt'
| f = open(out_file,'wb')
|
| word_presented = words.intersection(model.vocab.keys())
| f.write('{} {}\n'.format(len(word_presented),len(model['word'])))
|
| for word in word_presented:
| f.write('{} {}\n'.format(word, ' '.join(str(value) for value in model[word])))
|
| f.close()