Improving Paragraph2Vec
نویسنده
چکیده
Paragraph vectors were proposed as a powerful unsupervised method of learning representations of arbitrary lengths of text. Although paragraph vectors had the advantage of being versatile, being unsupervised and unconstrained by lengths of text, the concept has not been further developed since its first publication. We propose two extensions upon the initial formulation of the paragraph vector, and test its performance on two separate semantic-based tasks. Although the results are limited by the fact that our attempt to reproduce the original paragraph vectors was not successful, we can still show that the extended models outperform the original paragraph vectors.
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