Early Text Classification using Multi-Resolution Concept Representations

A. Pastor López-Monroy, Fabio A. González, Manuel Montes-y-Gómez, Hugo Jair Escalante and Thamar Solorio

TL;DR: Early Text Classification using Multi-Resolution Concept Representations

Keywords: Early Text Classification, Depressión Detection, Text Classification

Abstract: This poster explains a novel document representation presented at NAACL 2018 in NOLA. The representation is called Multi-Resolution Represen-tation (MulR), an aims to improve the early detection of risks in social media sources. The goal is to effectively identify the potential risk using as little evidence as possible and with as much anticipation as possible. MulR allows us to generate multiple “views” of the text. These views capture different semantic meanings for words and documents at different levels of granularity, which is very useful in early scenarios to model the variable amounts of evidence. The experimental evaluation shows that MulR using low resolution is better suited for modeling short documents (very early stages), whereas large documents (medium/late stages) are better modeled with higher resolutions. We evaluate the proposed ideas in two different tasks where anticipation is critical: sexual predator detection and depression detection. The experimental evaluation for these early tasks revealed that the proposed approach outperforms previous methodologies by a considerable margin. In this poster we will also explain some of our ongoing work related with the idea of concept representation applied to sentiment analysis and other text classification problems.