Research & Development
Our Research
Over the last 25 years, we have been conducting research and development on several topics related to AI including Natural-Language Processing (NLP), Text Analytics, Machine Learning, and Multi-Agent Systems.
Language Technologies for Intelligent Contact Centers
Companies use contact centers to interact with customers aiming to provide them with technical support and assistance. automated self-service solutions such as chatbots, virtual assistants fail to provide intelligence assistance and continuous interactions with users. In addition, it is unclear how complex natural-language questions can be answered or routed to humans. This project developed an advanced multi-component natural-language processing framework to allow automated call routing, which cannot be identified using simple speech-supported call centers.
Source: iText Routing: Reconocimiento inteligente de intencionaes de usuarios para enrutamiento de llamadas
Intelligent Fraud Detection for Power Supply Companies
Usually, fraud detection in non-registered power supply is based on decisions of various specialists and human analysts, who have low prediction accuracy in the task of predicting frauds. This project developed several AI methods that combined allow power supply fraud detection fraud with almost 3-4 times effectiveness than human analysts.
Source: Desarrollan en Chile Sistema Inteligente para detectar Fraudes Eléctricos
Brain Computer Interfaces for Emotion Recognition
Automatic EEG emotion recognition is usually restricted to a small number of emotions classes mainly due to signal’s features and noise, EEG constraints and subject-dependent issues. In order to address these issues, a novel feature-based emotion recognition model was developed for EEG-based Brain–Computer Interfaces using kernel-based automatic classifiers on standard EEG datasets.
Source: Improving BCI-based emotion recognition by combining EEG Feature Selection and Kernel Classifiers
Automated Essay Assessment using Natural-Language Discourse Analysis
This project combined discourse analysis, embedding learning (i.e., LSA, BERT) and semantic and syntactic models for automated essay assessment. The approach combines shallow linguistic features and discourse patterns in order to predict an essay’s score by using machine learning techniques Unlike current approaches, the method directly measures an essay coherence by using corpus-based semantics and text centering techniques so as to determine discourse patterns underlying high-quality essays when compared with human assessed essays.
Source: Coherence-Based Automatic Essay Assessment
Autonomous Multi-Agent Robotics
This project developed a behavior-based system for a team of four-legged autonomous robots (AIBO Sony). This involved game dynamic strategies, novel real-time computer vision systems, and negotiation techniques for coordinating intelligent agents on dynamic game conditions.
Source: UdeCans Team Description
Linguistically-motivated Opinion Mining
Social networks messaging typically contains a lot of implicit linguistic information. This may significantly impact several tasks including opinion mining and sentiment analysis, as opinion retrieval tasks will fail to obtain all the relevant messages. In order to address these issues, this project developed a novel adaptive approach for opinion retrieval that combines natural-language co-referencing techniques, and memory-based learning to resolving implicit co-referencing within informal opinion texts.