  Predicting which words are considered hard to understand for a given target
population is a vital step in many NLP applications such as text
simplification. This task is commonly referred to as Complex Word
Identification (CWI). With a few exceptions, previous studies have approached
the task as a binary classification task in which systems predict a complexity
value (complex vs. non-complex) for a set of target words in a text. This
choice is motivated by the fact that all CWI datasets compiled so far have been
annotated using a binary annotation scheme. Our paper addresses this limitation
by presenting the first English dataset for continuous lexical complexity
prediction. We use a 5-point Likert scale scheme to annotate complex words in
texts from three sources/domains: the Bible, Europarl, and biomedical texts.
This resulted in a corpus of 9,476 sentences each annotated by around 7
annotators.
