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Towards Single Word Lexical Complexity Prediction.

Conference paper
Authors David Alfter
Elena Volodina
Published in Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, New Orleans, Louisiana, June 5, 2018
ISBN 978-1-948087-11-7
Publisher Association of Computational Linguistics
Place of publication Stroudsburg, PA
Publication year 2018
Published at Department of Swedish
Language en
Links aclweb.org/anthology/W18-0508
Keywords automatic prediction of lexical complexity, machine learning, linguistic features
Subject categories Language Technology (Computational Linguistics), Specific Languages, General Language Studies and Linguistics

Abstract

In this paper we present work-in-progress where we investigate the usefulness of previously created word lists to the task of single-word lexical complexity analysis and prediction of the complexity level for learners of Swedish as a second language. The word lists used map each word to a single CEFR level, and the task consists of predicting CEFR levels for unseen words. In contrast to previous work on word-level lexical complexity, we experiment with topics as additional features and show that linking words to topics significantly increases accuracy of classification.

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