  We present a fairly large, Potential Idiomatic Expression (PIE) dataset for
Natural Language Processing (NLP) in English. The challenges with NLP systems
with regards to tasks such as Machine Translation (MT), word sense
disambiguation (WSD) and information retrieval make it imperative to have a
labelled idioms dataset with classes such as it is in this work. To the best of
the authors' knowledge, this is the first idioms corpus with classes of idioms
beyond the literal and the general idioms classification. In particular, the
following classes are labelled in the dataset: metaphor, simile, euphemism,
parallelism, personification, oxymoron, paradox, hyperbole, irony and literal.
We obtain an overall inter-annotator agreement (IAA) score, between two
independent annotators, of 88.89%. Many past efforts have been limited in the
corpus size and classes of samples but this dataset contains over 20,100
samples with almost 1,200 cases of idioms (with their meanings) from 10 classes
(or senses). The corpus may also be extended by researchers to meet specific
needs. The corpus has part of speech (PoS) tagging from the NLTK library.
Classification experiments performed on the corpus to obtain a baseline and
comparison among three common models, including the BERT model, give good
results. We also make publicly available the corpus and the relevant codes for
working with it for NLP tasks.
