# Implementing a Reverse Dictionary, based on word definitions, using a Node-Graph Architecture

arXiv: https://arxiv.org/abs/1606.00025 · HTML: https://mlsanity.com/paper/1606.00025/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2016.

## Built on by (most cited first, 8 of 8)

- [Semeval-2022 Task 1: CODWOE -- Comparing Dictionaries and Word Embeddings](https://mlsanity.com/paper/2205.13858/) (2022)
- [WordAlchemy: A transformer-based Reverse Dictionary](https://mlsanity.com/paper/2204.10181/) (2022)
- [PREDICT: Persian Reverse Dictionary](https://mlsanity.com/paper/2105.00309/) (2021)
- [IRB-NLP at SemEval-2022 Task 1: Exploring the Relationship Between Words and Their Semantic Representations](https://mlsanity.com/paper/2205.06840/) (2022)
- [3D-EX : A Unified Dataset of Definitions and Dictionary Examples](https://mlsanity.com/paper/2308.03043/) (2023)
- [Advancing Arabic Reverse Dictionary Systems: A Transformer-Based Approach with Dataset Construction Guidelines](https://mlsanity.com/paper/2504.21475/) (2025)
- [Idiomify -- Building a Collocation-supplemented Reverse Dictionary of English Idioms with Word2Vec for non-native learners](https://mlsanity.com/paper/2204.05634/) (2022)
- [Towards a Transformer-Based Reverse Dictionary Model for Quality Estimation of Definitions](https://mlsanity.com/paper/2311.02985/) (2023)

## Abstract (arXiv)

In this paper, we outline an approach to build graph-based reverse dictionaries using word definitions. A reverse dictionary takes a phrase as an input and outputs a list of words semantically similar to that phrase. It is a solution to the Tip-of-the-Tongue problem. We use a distance-based similarity measure, computed on a graph, to assess the similarity between a word and the input phrase. We compare the performance of our approach with the Onelook Reverse Dictionary and a distributional semantics method based on word2vec, and show that our approach is much better than the distributional semantics method, and as good as Onelook, on a 3k lexicon. This simple approach sets a new performance baseline for reverse dictionaries.