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What are some best ways to represent word for morphology tasks using unsupervised machine learning techniques?
What is GloVe, and how does it differ from Word2Vec?
GloVe is another popular method for word embedding that focuses on the global co-occurrence statistics of words in a corpus. It generates a word-document matrix, where each entry represents the probability of observing a word given a context word. GloVe then factorizes this matrix into a low-dimensional embedding space, which captures both the word similarities and their weighted co-occurrences. Overall, GloVe tends to perform better on some tasks than Word2Vec, especially when dealing with rare or infrequent words.