Complex Word Identification (CWI) is an
essential task in helping Lexical Simplification (LS) identify the difficult words
that should be simplified. In this paper,
we present an approach to CWI based
on Convolutional Neural Networks (CNN)
trained on pre-trained word embeddings
with morphological and linguistic features. Generally, the majority of works on
CWI are either feature-engineered or neural network with word embeddings. Both
approaches have advantages and limitations, so here we ...
Complex Word Identification (CWI) is an
essential task in helping Lexical Simplification (LS) identify the difficult words
that should be simplified. In this paper,
we present an approach to CWI based
on Convolutional Neural Networks (CNN)
trained on pre-trained word embeddings
with morphological and linguistic features. Generally, the majority of works on
CWI are either feature-engineered or neural network with word embeddings. Both
approaches have advantages and limitations, so here we combine both approaches
in order to achieve higher performance and
still support multilingualism. Our evaluation has shown that our system achieves
quite similar performance as the state-ofthe-art system for English, and it outperforms the state-of-the-art systems for both
Spanish and German.
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