Unsupervised, Knowledge-Free, and Interpretable Word Sense Disambiguation
This work addresses the interpretability gap in WSD for users who need transparent predictions, though it appears incremental by combining existing models.
The authors tackled the challenge of making word sense disambiguation (WSD) systems interpretable without relying on external knowledge, by developing a tool that bridges knowledge-based and knowledge-free methods, providing a web interface and API for human-readable predictions.
Interpretability of a predictive model is a powerful feature that gains the trust of users in the correctness of the predictions. In word sense disambiguation (WSD), knowledge-based systems tend to be much more interpretable than knowledge-free counterparts as they rely on the wealth of manually-encoded elements representing word senses, such as hypernyms, usage examples, and images. We present a WSD system that bridges the gap between these two so far disconnected groups of methods. Namely, our system, providing access to several state-of-the-art WSD models, aims to be interpretable as a knowledge-based system while it remains completely unsupervised and knowledge-free. The presented tool features a Web interface for all-word disambiguation of texts that makes the sense predictions human readable by providing interpretable word sense inventories, sense representations, and disambiguation results. We provide a public API, enabling seamless integration.