Textual inference applied to Question and Answering Systems

• Grant number: 13/22973-0
• Support Opportunities: Scholarships in Brazil - Doctorate
• Effective date (Start): May 01, 2014
• Effective date (End): February 28, 2018
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Sandra Maria Aluísio
• Grantee: Erick Rocha Fonseca
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Associated scholarship(s): 16/02466-5 - Deep neural networks for semantic alignments and recognizing textual entailment, BE.EP.DR

Abstract

This project aims the development of methods for the field of Question Answering (QA) and the implementation of a system that will employ them. Natural Language Processing techniques are intended to be explored in order to perform transformations in small portions of text, which should make them easier for processing, while keeping their original meaning. Thus, a QA system can have more flexibility when dealing with questions and text bases that talk about the same subjects, but written differently. In particular, the system is intended to be directed to the domain of chemical and contamination risk in the milk production chain, because this project is carried out in partnership with a research network about the domain and which involves other institutions.

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

FONSECA, ERICK; ALUISIO, SANDRA M.; VILLAVICENCIO, A; MOREIRA, V; ABAD, A; CASELI, H; GAMALLO, P; RAMISCH, C; OLIVEIRA, HG; PAETZOLD, GH. Syntactic Knowledge for Natural Language Inference in Portuguese. COMPUTATIONAL PROCESSING OF THE PORTUGUESE LANGUAGE, PROPOR 2018, v. 11122, p. 11-pg., 2018-01-01. (13/22973-0)

CRISCUOLO, MARCELO; FONSECA, ERICK ROCHA; ALUISIO, SANDRA MARIA; SPERANCA-CRISCUOLO, ANA CAROLINA; IEEE. MilkQA: a Dataset of Consumer Questions for the Task of Answer Selection. 2017 6TH BRAZILIAN CONFERENCE ON INTELLIGENT SYSTEMS (BRACIS), v. N/A, p. 6-pg., 2017-01-01. (13/22973-0)

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

FONSECA, Erick Rocha. Recognizing textual entailment in Portuguese. 2018. Doctoral Thesis - Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) São Carlos.