Jointly Learning Word Embeddings and Latent Topics

21 Jun 2017Bei ShiWai LamShoaib JameelSteven SchockaertKwun Ping Lai

Word embedding models such as Skip-gram learn a vector-space representation for each word, based on the local word collocation patterns that are observed in a text corpus. Latent topic models, on the other hand, take a more global view, looking at the word distributions across the corpus to assign a topic to each word occurrence... (read more)

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