AI-KU at SemEval-2016 task 11: word embeddings and substring features for complex word identification

Abstract

We investigate the usage of word embeddings, namely Glove and SCODE, along with substring features on Complex Word Identification task. We introduce two systems: the first system utilizes the word embeddings of the target word and its substrings as features while the other considers the context information by using the embeddings of the surrounding words as well. Although the proposed representations perform below the average with nonlinear models, we show that word embeddings with substring features is an effective representation choice when employed with linear classifiers.