Adaptation, Comparison, and Improvement of Metaheuristic Algorithms to the Part-of-Speech Tagging Problem

Part-of-Speech Tagging (POST) is a complex task in the preprocessing of Natural Language Processing applications. Tagging has been tackled from statistical information and rule-based approaches, making use of a range of methods. Most recently, metaheuristic algorithms have gained attention while bei...

Full description

Autores:
Tipo de recurso:
Fecha de publicación:
2020
Institución:
Universidad Pedagógica y Tecnológica de Colombia
Repositorio:
RiUPTC: Repositorio Institucional UPTC
Idioma:
eng
spa
OAI Identifier:
oai:repositorio.uptc.edu.co:001/14291
Acceso en línea:
https://revistas.uptc.edu.co/index.php/ingenieria/article/view/11762
https://repositorio.uptc.edu.co/handle/001/14291
Palabra clave:
computational intelligence
computational linguistics
evolutionary computing
heuristic algorithms
natural language processing
parts of speech tagging
search methods
algoritmos heurísticos
computación evolutiva
etiquetado de partes del discurso
inteligencia computacional
lingüística computacional
métodos de búsqueda
procesamiento de lenguaje natural
Rights
License
http://purl.org/coar/access_right/c_abf151
Description
Summary:Part-of-Speech Tagging (POST) is a complex task in the preprocessing of Natural Language Processing applications. Tagging has been tackled from statistical information and rule-based approaches, making use of a range of methods. Most recently, metaheuristic algorithms have gained attention while being used in a wide variety of knowledge areas, with good results. As a result, they were deployed in this research in a POST problem to assign the best sequence of tags (roles) for the words of a sentence based on information statistics. This process was carried out in two cycles, each of them comprised four phases, allowing the adaptation to the tagging problem in metaheuristic algorithms such as Particle Swarm Optimization, Jaya, Random-Restart Hill Climbing, and a memetic algorithm based on Global-Best Harmony Search as a global optimizer, and on Hill Climbing as a local optimizer. In the consolidation of each algorithm, preliminary experiments were carried out (using cross-validation) to adjust the parameters of each algorithm and, thus, evaluate them on the datasets of the complete tagged corpus: IULA (Spanish), Brown (English) and Nasa Yuwe (Nasa). The results obtained by the proposed taggers were compared, and the Friedman and Wilcoxon statistical tests were applied, confirming that the proposed memetic, GBHS Tagger, obtained better results in precision. The proposed taggers make an important contribution to POST for traditional languages (English and Spanish), non-traditional languages (Nasa Yuwe), and their application areas.