Skip to content
conftrace_
2018 NAACL NAACL 2018

Complex Word Identification Based on Frequency in a Learner Corpus

Abstract

AbstractWe introduce the TMU systems for the Complex Word Identification (CWI) Shared Task 2018. TMU systems use random forest classifiers and regressors whose features are the number of characters, the number of words, and the frequency of target words in various corpora. Our simple systems performed best on 5 tracks out of 12 tracks. Our ablation analysis revealed the usefulness of a learner corpus for CWI task.

Authors

Tomoyuki Kajiwara, Mamoru Komachi

Topics

Machine Learning > Core Methods > Classification Machine Learning > Core Methods > Regression

Keywords

feature extraction random forest complex word identification learner corpus
Download PDF

More from NAACL 2018

All NAACL 2018 papers →
Cross-lingual complex word identification with multitask learning
UnibucKernel: A kernel-based learning method for complex word identification
CAMB at CWI Shared Task 2018: Complex Word Identification with Ensemble-Based Voting
The Whole is Greater than the Sum of its Parts: Towards the Effectiveness of Voting Ensemble Classifiers for Complex Word Identification
Grotoco@SLAM: Second Language Acquisition Modeling with Simple Features, Learners and Task-wise Models
Context Based Approach for Second Language Acquisition