# Classifying Idiomatic and Literal Expressions Using Topic Models and Intensity of Emotions

arXiv: https://arxiv.org/abs/1802.09961 · HTML: https://mlsanity.com/paper/1802.09961/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2018.

## Built on by (most cited first, 13 of 13)

- [Idiomatic Expression Identification using Semantic Compatibility](https://mlsanity.com/paper/2110.10064/) (2021)
- [Few-shot Anomaly Detection in Text with Deviation Learning](https://mlsanity.com/paper/2308.11780/) (2023)
- [Getting BART to Ride the Idiomatic Train: Learning to Represent Idiomatic Expressions](https://mlsanity.com/paper/2207.03679/) (2022)
- [Idioms, Probing and Dangerous Things: Towards Structural Probing for Idiomaticity in Vector Space](https://mlsanity.com/paper/2304.14333/) (2023)
- [Topic Aware Probing: From Sentence Length Prediction to Idiom Identification how reliant are Neural Language Models on Topic?](https://mlsanity.com/paper/2403.02009/) (2024)
- [Characterizing Idioms: Conventionality and Contingency](https://mlsanity.com/paper/2104.08664/) (2021)
- [No Pattern, No Recognition: a Survey about Reproducibility and Distortion Issues of Text Clustering and Topic Modeling](https://mlsanity.com/paper/2208.01712/) (2022)
- [Unified Representation for Non-compositional and Compositional Expressions](https://mlsanity.com/paper/2310.19127/) (2023)
- [A Survey of Idiom Datasets for Psycholinguistic and Computational Research](https://mlsanity.com/paper/2508.11828/) (2025)
- [Expanding the Role of Affective Phenomena in Multimodal Interaction Research](https://mlsanity.com/paper/2305.10827/) (2023)
- [IEKG: A Commonsense Knowledge Graph for Idiomatic Expressions](https://mlsanity.com/paper/2312.06053/) (2023)
- [Sentiment-based Candidate Selection for NMT](https://mlsanity.com/paper/2104.04840/) (2021)
- [HiJoNLP at SemEval-2022 Task 2: Detecting Idiomaticity of Multiword Expressions using Multilingual Pretrained Language Models](https://mlsanity.com/paper/2205.13708/) (2022)

## Abstract (arXiv)

We describe an algorithm for automatic classification of idiomatic and literal expressions. Our starting point is that words in a given text segment, such as a paragraph, that are highranking representatives of a common topic of discussion are less likely to be a part of an idiomatic expression. Our additional hypothesis is that contexts in which idioms occur, typically, are more affective and therefore, we incorporate a simple analysis of the intensity of the emotions expressed by the contexts. We investigate the bag of words topic representation of one to three paragraphs containing an expression that should be classified as idiomatic or literal (a target phrase). We extract topics from paragraphs containing idioms and from paragraphs containing literals using an unsupervised clustering method, Latent Dirichlet Allocation (LDA) (Blei et al., 2003). Since idiomatic expressions exhibit the property of non-compositionality, we assume that they usually present different semantics than the words used in the local topic. We treat idioms as semantic outliers, and the identification of a semantic shift as outlier detection. Thus, this topic representation allows us to differentiate idioms from literals using local semantic contexts. Our results are encouraging.