# That's sick dude!: Automatic identification of word sense change across different timescales

arXiv: https://arxiv.org/abs/1405.4392 · HTML: https://mlsanity.com/paper/1405.4392/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2014.

## Built on by (most cited first, 11 of 11)

- [Simple, Interpretable and Stable Method for Detecting Words with Usage Change across Corpora](https://mlsanity.com/paper/2112.14330/) (2021)
- [Statistically significant detection of semantic shifts using contextual word embeddings](https://mlsanity.com/paper/2104.03776/) (2021)
- [Lexical semantic change for Ancient Greek and Latin](https://mlsanity.com/paper/2101.09069/) (2021)
- [On the Universality of Deep Contextual Language Models](https://mlsanity.com/paper/2109.07140/) (2021)
- [Tweet Insights: A Visualization Platform to Extract Temporal Insights from Twitter](https://mlsanity.com/paper/2308.02142/) (2023)
- [Syntactic Language Change in English and German: Metrics, Parsers, and Convergences](https://mlsanity.com/paper/2402.11549/) (2024)
- [AXOLOTL'24 Shared Task on Multilingual Explainable Semantic Change Modeling](https://mlsanity.com/paper/2407.04079/) (2024)
- [Measuring diachronic sense change: new models and Monte Carlo methods for Bayesian inference](https://mlsanity.com/paper/2105.00819/) (2021)
- [An Embedded Diachronic Sense Change Model with a Case Study from Ancient Greek](https://mlsanity.com/paper/2311.00541/) (2023)
- [Enhancing AI Research Paper Analysis: Methodology Component Extraction using Factored Transformer-based Sequence Modeling Approach](https://mlsanity.com/paper/2311.03401/) (2023)
- [Computational conceptual history of scientific concepts: From early digital methods to LLMs](https://mlsanity.com/paper/2606.04118/) (2026)

## Abstract (arXiv)

In this paper, we propose an unsupervised method to identify noun sense changes based on rigorous analysis of time-varying text data available in the form of millions of digitized books. We construct distributional thesauri based networks from data at different time points and cluster each of them separately to obtain word-centric sense clusters corresponding to the different time points. Subsequently, we compare these sense clusters of two different time points to find if (i) there is birth of a new sense or (ii) if an older sense has got split into more than one sense or (iii) if a newer sense has been formed from the joining of older senses or (iv) if a particular sense has died. We conduct a thorough evaluation of the proposed methodology both manually as well as through comparison with WordNet. Manual evaluation indicates that the algorithm could correctly identify 60.4% birth cases from a set of 48 randomly picked samples and 57% split/join cases from a set of 21 randomly picked samples. Remarkably, in 44% cases the birth of a novel sense is attested by WordNet, while in 46% cases and 43% cases split and join are respectively confirmed by WordNet. Our approach can be applied for lexicography, as well as for applications like word sense disambiguation or semantic search.