@mastersthesis{development-gsi-mastersthesis-20184,
author = "Carlander-Reuterfelt Gallo, Daniel ",
abstract = "The application of natural language to improve the interaction of human users with infor-
mation systems is a growing trend in the recent years. Advances in cognitive computing
enable a new way of interaction that accelerates insight from existing information sources.
In this project, a modular cognitive agent architecture for question answering featuring social dialogue (small talk) improved for a specific knowledge domain is designed and
developed. The proposed system has been implemented as a personal agent to assist stu-
dents learning Data Science and Machine Learning techniques. To that end, a responsive
web interface was developed. Also, a Knowledge Base was provided with all the necessary
information.
The developed prototype has been evaluated to analyze how users perceive the inter-
action with the system. We claimed that the inclusion of social dialogue results in better
responses and engaging experiences to users.
In the end, the evaluation results that support our hypothesis are presented, as well as
thoughts in future work.",
address = "ETSI Telecomunicaci{\'o}n",
institution = "Universidad Polit{\'e}cnica de Madrid",
keywords = "cognitive agent;e-learning;education;innovaci{\'o}n educativa;question-answering",
month = "June",
title = "{D}evelopment of a {C}ognitive {B}ot for {D}ata {S}cience tutoring based on a {B}ig {D}ata {N}atural {L}anguage {A}nalytics {P}latform",
type = "TFG",
year = "2019",
}