학술논문

Chatbots: Autoexpansion Approach to Improve Natural Language Automatic Dialogs
Document Type
Conference
Source
2020 IEEE Congreso Bienal de Argentina (ARGENCON) Argentina (ARGENCON), 2020 IEEE Congreso Bienal de. :1-9 Dec, 2020
Subject
Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
Geoscience
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Training
Statistical analysis
Prototypes
Machine learning
Linguistics
Chatbots
Software
Intelligent Systems
Soft Computing
Data Mining
Human-Machine Interface
Natural Language Processing
Morphosyntactic Linguistic Wavelets
Language
Abstract
Chatbots belong to a large family of software robots that aim to gracefully integrate human spoken interactions as interface. Most of the proposals in the field intend to solve severe limitations of the automatic processing of natural language. This paper is part of a project called PTAH, that implements a prototype able to ask and answer about a topic in Spanish. Although previous work solved much of the training and language-based strategies, there is still poor resources to make the bot understand alternatives to answer specific questions or problems. This paper presents an approach called auto-expansion with an original combination of Morphosyntactic-Linguistic-Wavelets and certain Machine Learning techniques. As part of the scope, the basic preliminary theory is introduced, along with detailed description of the self-expansion proposal and some of the prototype's state of implementation, tests and statistical analysis. Authors intend to show the goodness of the proposal.