Implementing a Reverse Dictionary, based on word definitions, using a Node-Graph Architecture
This provides a solution for users struggling to recall words, though it is incremental as it builds on existing reverse dictionary concepts.
The paper tackles the Tip-of-the-Tongue problem by building a graph-based reverse dictionary that outputs words similar to an input phrase, showing it performs as well as Onelook and better than a word2vec method on a 3k lexicon.
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.