El modelo cortical HTM y su aplicación al conocimiento lingüístico

  1. Iván Arias Rodríguez
Supervised by:
  1. Ana Fernández-Pampillón Cesteros Director
  2. Eugenio Ramón Luján Martínez Director

Defence university: Universidad Complutense de Madrid

Year of defence: 2022

  1. María Victoria Escandell Vidal Chair
  2. José Luis Sierra Rodríguez Secretary
  3. Juan Manuel Cigarrán Recuero Committee member
  4. Pedro Manuel Rangel Santos Henriques Committee member
  5. Olga Batiukova Committee member
  1. Lingüística, Estudios Árabes, Hebreos, Vascos y de Asia Oriental

Type: Thesis


The problem addressed by this research work is that of finding a neurocomputational representation and comprehension model of lexical knowledge, using for that purpose the HTM cortical algorithm, which models the mechanism according to which information is processed in the human neocortex.The automatic understanding of natural language implies that machines have a deep knowledge of natural language, which is currently far from being achieved. In general, computational models for Natural Language Processing (NLP), both in their analysis and comprehension aspects as well as in the generation aspect, use algorithms based on mathematical and linguistic models that try to emulate the way in which language has traditionally been processed, for example, by obtaining the implicit hierarchical structure of sentences or word endings. These models are useful because they serve to build concrete applications such as data mining, text classification or sentiment analysis. However, despite their usefulness, machines do not really understand what they are doing with any of these models. The question, therefore, addressed in this thesis is whether it is really possible to computationally model the human neocortical processes that regulate the processing of lexical semantic information. This research question constitutes the first level of understanding the processing of natural language at higher linguistic levels...