Improving Word Embeddings Using Kernel PCA

WS 2019 Vishwani GuptaSven GiesselbachStefan R{\"u}pingChristian Bauckhage

Word-based embedding approaches such as Word2Vec capture the meaning of words and relations between them, particularly well when trained with large text collections; however, they fail to do so with small datasets. Extensions such as fastText reduce the amount of data needed slightly, however, the joint task of learning meaningful morphology, syntactic and semantic representations still requires a lot of data... (read more)

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