2021
ACL
ACL 2021
LangResearchLab NC at SemEval-2021 Task 1: Linguistic Feature Based Modelling for Lexical Complexity
Abstract
AbstractThe present work aims at assigning a complexity score between 0 and 1 to a target word or phrase in a given sentence. For each Single Word Target, a Random Forest Regressor is trained on a feature set consisting of lexical, semantic, and syntactic information about the target. For each Multiword Target, a set of individual word features is taken along with single word complexities in the feature space. The system yielded the Pearson correlation of 0.7402 and 0.8244 on the test set for the Single and Multiword Targets, respectively.
🌉
Interdisciplinary Bridge
— Machine Learning and Natural Language Processing
🧭
Keyword Pioneer
— random forest regressor
🐝
Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Keywords
feature extraction
lexical complexity
semantic analysis
syntactic analysis
semantic information
pearson correlation
feature engineering
regression model
random forest
linguistic feature
semantic feature
syntactic feature
random forest regressor
random forest regression
pearson correlation coefficient
lexical complexity prediction
linguistic feature engineering