CompLex: A New Corpus for Lexical Complexity Prediction from Likert Scale Data
This addresses the problem of text simplification for NLP applications by providing a more nuanced dataset, though it is incremental as it builds on existing CWI work.
The paper tackles the limitation of binary annotation in Complex Word Identification (CWI) by introducing CompLex, the first English dataset for continuous lexical complexity prediction using a 5-point Likert scale, resulting in a corpus of 9,476 sentences annotated by around 7 annotators.
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.