CLaC at SemEval-2016 Task 11: Exploring linguistic and psycho-linguistic Features for Complex Word Identification

TL;DR

This paper presents a system for complex word identification using linguistic and psycho-linguistic features, achieving competitive results in SemEval-2016 by employing supervised models, notably Random Forests.

Contribution

The study explores the effectiveness of linguistic and cognitive features in identifying complex words, with a focus on feature engineering and model comparison.

Findings

Random Forest outperformed other models

Achieved a G-score of 68.8%

Ranked 21st out of 45 systems

Abstract

This paper describes the system deployed by the CLaC-EDLK team to the "SemEval 2016, Complex Word Identification task". The goal of the task is to identify if a given word in a given context is "simple" or "complex". Our system relies on linguistic features and cognitive complexity. We used several supervised models, however the Random Forest model outperformed the others. Overall our best configuration achieved a G-score of 68.8% in the task, ranking our system 21 out of 45.