Advanced Transfer Learning Approach for Improving Spanish Sentiment Analysis

6Citations
Citations of this article
9Readers
Mendeley users who have this article in their library.
Get full text

Abstract

In the last years, innovative techniques like Transfer Learning have impacted strongly in Natural Language Processing, increasing massively the state-of-the-art in several challenging tasks. In particular, the Universal Language Model Fine-Tuning (ULMFiT) algorithm has proven to have an impressive performance on several English text classification tasks. In this paper, we aim at developing an algorithm for Spanish Sentiment Analysis of short texts that is comparable to the state-of-the-art. In order to do so, we have adapted the ULMFiT algorithm to this setting. Experimental results on benchmark datasets (InterTASS 2017 and InterTASS 2018) show how this simple transfer learning approach performs well when compared to fancy deep learning techniques.

Cite

CITATION STYLE

APA

Palomino, D., & Ochoa-Luna, J. (2019). Advanced Transfer Learning Approach for Improving Spanish Sentiment Analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11835 LNAI, pp. 112–123). Springer. https://doi.org/10.1007/978-3-030-33749-0_10

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free