# Potential Idiomatic Expression (PIE)-English: Corpus for Classes of Idioms

arXiv: https://arxiv.org/abs/2105.03280 · HTML: https://mlsanity.com/paper/2105.03280/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 35 references that are in the map.
- First posted on arXiv 25 Apr 2021.

## Builds on (most cited first, 5 of 5)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [A Survey on Data Collection for Machine Learning: a Big Data -- AI Integration Perspective](https://mlsanity.com/paper/1811.03402/) (2018)
- [EPIE Dataset: A Corpus For Possible Idiomatic Expressions](https://mlsanity.com/paper/2006.09479/) (2020)
- [Word2Vec: Optimal Hyper-Parameters and Their Impact on NLP Downstream Tasks](https://mlsanity.com/paper/2003.11645/) (2020)

## Built on by (most cited first, 15 of 15)

- [Translate Meanings, Not Just Words: IdiomKB's Role in Optimizing Idiomatic Translation with Language Models](https://mlsanity.com/paper/2308.13961/) (2023)
- [Introducing Rhetorical Parallelism Detection: A New Task with Datasets, Metrics, and Baselines](https://mlsanity.com/paper/2312.00100/) (2023)
- [Memorization or Reasoning? Exploring the Idiom Understanding of LLMs](https://mlsanity.com/paper/2505.16216/) (2025)
- [Vector Representations of Idioms in Conversational Systems](https://mlsanity.com/paper/2205.03666/) (2022)
- [Getting BART to Ride the Idiomatic Train: Learning to Represent Idiomatic Expressions](https://mlsanity.com/paper/2207.03679/) (2022)
- [Isolating Culture Neurons in Multilingual Large Language Models](https://mlsanity.com/paper/2508.02241/) (2025)
- [A Survey of Idiom Datasets for Psycholinguistic and Computational Research](https://mlsanity.com/paper/2508.11828/) (2025)
- [Is Less More? Quality, Quantity and Context in Idiom Processing with Natural Language Models](https://mlsanity.com/paper/2405.08497/) (2024)
- [Killing Two Flies with One Stone: An Attempt to Break LLMs Using English->Icelandic Idioms and Proper Names](https://mlsanity.com/paper/2410.03394/) (2024)
- [SinFoS: A Parallel Dataset for Translating Sinhala Figures of Speech](https://mlsanity.com/paper/2602.09866/) (2026)
- [Figuratively Speaking: Authorship Attribution via Multi-Task Figurative Language Modeling](https://mlsanity.com/paper/2406.08218/) (2024)
- [Computational Approaches to the Detection of Lesser-Known Rhetorical Figures: A Systematic Survey and Research Challenges](https://mlsanity.com/paper/2406.16674/) (2024)
- [Enhancing Rhetorical Figure Annotation: An Ontology-Based Web Application with RAG Integration](https://mlsanity.com/paper/2412.13799/) (2024)
- [From the Rock Floor to the Cloud: A Systematic Survey of State-of-the-Art NLP in Battery Life Cycle](https://mlsanity.com/paper/2510.27369/) (2025)
- [Towards High-Level Semantic Intelligence](https://mlsanity.com/paper/2607.24082/) (2026)

## Abstract (arXiv)

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.