Investigating Antigram Behaviour using Distributional Semantics
This addresses a niche linguistic curiosity for computational linguistics researchers, but it is highly incremental with minimal practical impact.
The paper tackled the problem of detecting antigrams (semantically opposite anagrams) using GloVe embeddings and a rule-based algorithm, achieving 39% accuracy on a small dataset of 12 antigrams.
The field of computational linguistics constantly presents new challenges and topics for research. Whether it be analyzing word usage changes over time or identifying relationships between pairs of seemingly unrelated words. To this point, we identify Anagrams and Antigrams as words possessing such unique properties. The presented work is an exploration into generating anagrams from a given word and determining whether there exists antigram (semantically opposite anagrams) relationships between the pairs of generated anagrams using GloVe embeddings. We propose a rudimentary, yet interpretable, rule-based algorithm for detecting antigrams. On a small dataset of just 12 antigrams, our approach yielded an accuracy of 39\% which shows that there is much work left to be done in this space.