What methods are there to cluster words/word phrases with similar meanings together from a list of words/word phrases?


1 Answer 1


At least one approach to this question would be to create word embeddings, apply PCA, and then use TSNE or K-means to cluster words with similar meanings. Word embeddings can be created using Word2Vec or GloVE.

from gensim.models import word2vec


model = word2vec.Word2Vec(corpus, size=100, window=20, min_count=2, workers=16)
    keys = ["markets", "exchanges", "otc","stocks", "equity","indices", "ipo","commodity", "mortgages", "abs", "derivatives"]
    embedding_clusters = []
    word_clusters = []
    for word in keys:
        embeddings = []
        words = []
        for similar_word, _ in model.wv.most_similar(word, topn=15):


embedding_clusters = np.array(embedding_clusters)
n, m, k = embedding_clusters.shape
tsne_model_en_2d = TSNE(perplexity=20, n_components=2, init='pca', n_iter=3500, random_state=42)
embeddings_en_2d = np.array(tsne_model_en_2d.fit_transform(embedding_clusters.reshape(n * m, k))).reshape(n, m, 2)

def tsne_plot_similar_words(title, labels, embedding_clusters, word_clusters, alpha, filename=None):
    plt.figure(figsize=(16, 9))
    colors = cm.rainbow(np.linspace(0, 1, len(labels)))
    for label, embeddings, words, color in zip(labels, embedding_clusters, word_clusters, colors):
        x = embeddings[:, 0]
        y = embeddings[:, 1]
        plt.scatter(x, y, color=color, alpha=alpha, label=label)
        for i, word in enumerate(words):
            plt.annotate(word, alpha=0.5, xy=(x[i], y[i]), xytext=(5, 2),
                         textcoords='offset points', ha='right', va='bottom', size=8)
    if filename:
        plt.savefig(filename, format='png', dpi=150, bbox_inches='tight')

tsne_plot_similar_words('Similar words', keys, embeddings_en_2d, word_clusters, 0.7,
                        'other words.png')

enter image description here


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