# Fighting with the Sparsity of Synonymy Dictionaries

arXiv: https://arxiv.org/abs/1708.09234 · HTML: https://mlsanity.com/paper/1708.09234/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2017.

## Built on by (most cited first, 1 of 1)

- [Current Trends and Approaches in Synonyms Extraction: Potential Adaptation to Arabic](https://mlsanity.com/paper/2205.10412/) (2022)

## Abstract (arXiv)

Graph-based synset induction methods, such as MaxMax and Watset, induce synsets by performing a global clustering of a synonymy graph. However, such methods are sensitive to the structure of the input synonymy graph: sparseness of the input dictionary can substantially reduce the quality of the extracted synsets. In this paper, we propose two different approaches designed to alleviate the incompleteness of the input dictionaries. The first one performs a pre-processing of the graph by adding missing edges, while the second one performs a post-processing by merging similar synset clusters. We evaluate these approaches on two datasets for the Russian language and discuss their impact on the performance of synset induction methods. Finally, we perform an extensive error analysis of each approach and discuss prominent alternative methods for coping with the problem of the sparsity of the synonymy dictionaries.