Semantic Representation for Text Classification

• Grant number: 16/07620-2
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate
• Start date: August 01, 2016
• End date: January 31, 2017
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Solange Oliveira Rezende
• Grantee: Roberta Akemi Sinoara
• Supervisor: Roberto Navigli
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Institution abroad: Università degli Studi di Roma La Sapienza, Italy
• Associated to the scholarship: 13/14757-6 - Incorporating the semantics into the websensors construction process, BP.DR

Abstract

In text mining, traditional text representation are based on the frequency of words in the documents. Although good results for automatic text classification can be achieved with the use of this bag-of-words representation, such representation model is not suitable for all classification problems and richer text representations can be required. The objective of this internship project is to develop a semantic text representation based on NASARI approach. NASARI is a concept representation used to measure semantic similarity with good results in word similarity and sense clustering tasks. It is based on knowledge from both WordNet and Wikipedia. Thus, this project aims to enhance document representation with the semantically rich NASARI concept representation. The proposed text representation will be evaluated in text classification tasks. It is expected that the use of a NASARI-based text representation will improve text classification performance. This project is closely related to the student's doctoral project in development at Universidade de São Paulo. The internship project will be developed at Sapienza - Università di Roma, under the supervision of professor Roberto Navigli, who is one of the authors of NASARI approach.

Scientific publications

(The scientific publications listed on this page originate from the Web of Science or SciELO databases. Their authors have cited FAPESP grant or fellowship project numbers awarded to Principal Investigators or Fellowship Recipients, whether or not they are among the authors. This information is collected automatically and retrieved directly from those bibliometric databases.)

SINOARA, ROBERTA A.; CAMACHO-COLLADOS, JOSE; ROSSI, RAFAEL G.; NAVIGLI, ROBERTO; REZENDE, SOLANGE O.. Knowledge-enhanced document embeddings for text classification. KNOWLEDGE-BASED SYSTEMS, v. 163, p. 955-971, JAN 1 2019. (16/17078-0, 13/14757-6, 16/07620-2)

SINOARA, ROBERTA A.; ROSSI, RAFAEL G.; REZENDE, SOLANGE O.; IEEE. Semantic Role-based Representations in Text Classification. 2016 23RD INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), v. N/A, p. 6-pg., 2016-01-01. (16/07620-2, 14/08996-0, 11/12823-6, 13/14757-6)