Deep Learning Architecture for Complex Word Identification

Dirk De Hertog, Anaïs Tack


Abstract
We describe a system for the CWI-task that includes information on 5 aspects of the (complex) lexical item, namely distributional information of the item itself, morphological structure, psychological measures, corpus-counts and topical information. We constructed a deep learning architecture that combines those features and apply it to the probabilistic and binary classification task for all English sets and Spanish. We achieved reasonable performance on all sets with best performances seen on the probabilistic task, particularly on the English news set (MAE 0.054 and F1-score of 0.872). An analysis of the results shows that reasonable performance can be achieved with a single architecture without any domain-specific tweaking of the parameter settings and that distributional features capture almost all of the information also found in hand-crafted features.
Anthology ID:
W18-0539
Volume:
Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venues:
BEA | NAACL | WS
SIG:
SIGEDU
Publisher:
Association for Computational Linguistics
Note:
Pages:
328–334
Language:
URL:
https://www.aclweb.org/anthology/W18-0539
DOI:
10.18653/v1/W18-0539
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-0539.pdf