Word Embeddings Based on Spectral Analysis: A Novel Approach
Abstract
Recently, deep learning algorithms have gained huge attention. However, such algorithms are not the optimal solution for many tasks. Spectral analysis transformation algorithms, such as wavelet-transform and Fourier transform, have been successfully applied on many NLP tasks. The challenging issue of using spectral analysis is how to construct a meaningful signal from a text. In word2vec models, different types of neural networks have been applied to learn vector representations of words, which carry the semantic similarities of each word in a specific dataset. Training the word embeddings is computationally very expensive and constrained by the available resources. However, this paper provides an optimized computational complexity for developing word embeddings using parallel computing as well as utilizing an upper ontology to represent the main components of the word vector. Moreover, this research shows how to represent a term as a vector to facilitate computing the similarity or relatedness among different terms. Therefore, this research considers the spectral analysis, which also includes the spatial information of the words around the current word.References
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