# Watset: Automatic Induction of Synsets from a Graph of Synonyms

arXiv: https://arxiv.org/abs/1704.07157 · HTML: https://mlsanity.com/paper/1704.07157/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2017.

## Built on by (most cited first, 5 of 5)

- [Evaluation Gaps in Machine Learning Practice](https://mlsanity.com/paper/2205.05256/) (2022)
- [Crosslingual Transfer Learning for Low-Resource Languages Based on Multilingual Colexification Graphs](https://mlsanity.com/paper/2305.12818/) (2023)
- [Current Trends and Approaches in Synonyms Extraction: Potential Adaptation to Arabic](https://mlsanity.com/paper/2205.10412/) (2022)
- [Sem4SAP: Synonymous Expression Mining From Open Knowledge Graph For Language Model Synonym-Aware Pretraining](https://mlsanity.com/paper/2303.14425/) (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)

## Abstract (arXiv)

This paper presents a new graph-based approach that induces synsets using synonymy dictionaries and word embeddings. First, we build a weighted graph of synonyms extracted from commonly available resources, such as Wiktionary. Second, we apply word sense induction to deal with ambiguous words. Finally, we cluster the disambiguated version of the ambiguous input graph into synsets. Our meta-clustering approach lets us use an efficient hard clustering algorithm to perform a fuzzy clustering of the graph. Despite its simplicity, our approach shows excellent results, outperforming five competitive state-of-the-art methods in terms of F-score on three gold standard datasets for English and Russian derived from large-scale manually constructed lexical resources.