Semantic text similarity using autoencoders

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Title: Semantic text similarity using autoencoders
Author: Nikola, Mandic
Contributor: University of Helsinki, Faculty of Science, Department of Computer Science
Publisher: Helsingin yliopisto
Date: 2018
Language: eng
Thesis level: master's thesis
Discipline: Computer science
Abstract: Word vectors have become corner stone of modern NLP. Researchers are taking embedding ever further by learning to craft embedding vectors with task specific semantics to power wide array of applications. In this thesis we apply simple feed forward network and stacked LSTM on triplets dataset converted to sentence embeddings to evaluate paragraph semantic text similarity. We explore how to leverage existing state of the art sentence embeddings for paragraph semantic text similarity and examine information sentence embeddings used hold.

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