Work

katildakat at SemEval-2021 Task 1: Lexical Complexity Prediction of Single Words and Multi-Word Expressions in English

Year: 2021

Type: conference-paper

Abstract: This paper describes systems submitted to Se-mEval 2021 Task 1: Lexical Complexity Prediction (LCP).We compare a linear and a nonlinear regression models trained to work for both tracks of the task.We show that both systems are able to generalize better when supplied with information about complexities of single word and multi-word expression (MWE) targets simultaneously.This approach proved to be the most beneficial for multiword expression targets.We also demonstrate that some hand-crafted features differ in their importance for the target types.

Source: International Workshop on Semantic Evaluation (SemEval)

DOI: 10.18653/v1/2021.semeval-1.91

Author: Katja Voskoboinik

Institution: Aalto University

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Academy of Finland

Award: G796713724