# Computing Lexical Contrast

arXiv: https://arxiv.org/abs/1308.6300 · HTML: https://mlsanity.com/paper/1308.6300/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2013.

## Built on by (most cited first, 7 of 7)

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- [Permutation invariant matrix statistics and computational language tasks](https://mlsanity.com/paper/2202.06829/) (2022)
- [ARCOQ: Arabic Closest Opposite Questions Dataset](https://mlsanity.com/paper/2310.14384/) (2023)
- [Beyond Cosine Similarity: Taming Semantic Drift and Antonym Intrusion in a 15-Million Node Turkish Synonym Graph](https://mlsanity.com/paper/2601.13251/) (2026)
- [A Hybrid Protocol for Large-Scale Semantic Dataset Generation in Low-Resource Languages: The Turkish Semantic Relations Corpus](https://mlsanity.com/paper/2601.13253/) (2026)
- [Disentangling Similarity and Relatedness in Topic Models](https://mlsanity.com/paper/2603.10619/) (2026)

## Abstract (arXiv)

Knowing the degree of semantic contrast between words has widespread application in natural language processing, including machine translation, information retrieval, and dialogue systems. Manually-created lexicons focus on opposites, such as rm hot and rm cold. Opposites are of many kinds such as antipodals, complementaries, and gradable. However, existing lexicons often do not classify opposites into the different kinds. They also do not explicitly list word pairs that are not opposites but yet have some degree of contrast in meaning, such as rm warm and rm cold or rm tropical and rm freezing. We propose an automatic method to identify contrasting word pairs that is based on the hypothesis that if a pair of words, A and B, are contrasting, then there is a pair of opposites, C and D, such that A and C are strongly related and B and D are strongly related. (For example, there exists the pair of opposites rm hot and rm cold such that rm tropical is related to rm hot, and rm freezing is related to rm cold.) We will call this the contrast hypothesis. We begin with a large crowdsourcing experiment to determine the amount of human agreement on the concept of oppositeness and its different kinds. In the process, we flesh out key features of different kinds of opposites. We then present an automatic and empirical measure of lexical contrast that relies on the contrast hypothesis, corpus statistics, and the structure of a it Roget-like thesaurus. We show that the proposed measure of lexical contrast obtains high precision and large coverage, outperforming existing methods.