Publicación:
Advanced Transfer Learning Approach for Improving Spanish Sentiment Analysis

dc.contributor.author Palomino D. es_PE
dc.contributor.author Ochoa-Luna J. es_PE
dc.date.accessioned 2024-05-30T23:13:38Z
dc.date.available 2024-05-30T23:13:38Z
dc.date.issued 2019
dc.description.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. © Springer Nature Switzerland AG 2019.
dc.description.sponsorship Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - Concytec
dc.identifier.doi https://doi.org/10.1007/978-3-030-33749-0_10
dc.identifier.scopus 2-s2.0-85075647488
dc.identifier.uri https://hdl.handle.net/20.500.12390/2725
dc.language.iso eng
dc.publisher Springer
dc.relation.ispartof Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.rights info:eu-repo/semantics/openAccess
dc.subject Transfer learning
dc.subject Language Model es_PE
dc.subject Natural Language Processing es_PE
dc.subject Sentiment analysis es_PE
dc.subject.ocde http://purl.org/pe-repo/ocde/ford#2.02.04
dc.title Advanced Transfer Learning Approach for Improving Spanish Sentiment Analysis
dc.type info:eu-repo/semantics/article
dspace.entity.type Publication
Archivos