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arXiv · 1709.00947

Learning Word Embeddings from the Portuguese Twitter Stream: A Study of some Practical Aspects

Pedro Saleiro·Luís Sarmento·Eduarda Mendes Rodrigues·Carlos Soares·Eugénio Oliveira
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Abstract

This paper describes a preliminary study for producing and distributing a large-scale database of embeddings from the Portuguese Twitter stream. We start by experimenting with a relatively small sample and focusing on three challenges: volume of training data, vocabulary size and intrinsic evaluation metrics. Using a single GPU, we were able to scale up vocabulary size from 2048 words embedded and 500K training examples to 32768 words over 10M training examples while keeping a stable validation loss and approximately linear trend on training time per epoch. We also observed that using less than 50\% of the available training examples for each vocabulary size might result in overfitting. Results on intrinsic evaluation show promising performance for a vocabulary size of 32768 words. Nevertheless, intrinsic evaluation metrics suffer from over-sensitivity to their corresponding cosine similarity thresholds, indicating that a wider range of metrics need to be developed to track progress.

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BibTeXRIS

Pedro Saleiro, Luís Sarmento, Eduarda Mendes Rodrigues, Carlos Soares, Eugénio Oliveira. 2017-09-04. Learning Word Embeddings from the Portuguese Twitter Stream: A Study of some Practical Aspects. https://arxiv.org/abs/1709.00947

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