Speakers
Description
Finding non-recorded senses is important for dictionary maintenance, where using automatic methods helps reduce manual efforts. We use automatic Word Sense Induction (WSI) to compare recorded sense numbers among a sample of headwords in a comprehensive Swedish monolingual dictionary with induced sense numbers for the same words in a Swedish corpus. We propose this as a simple technique to find words to prioritize for post-hoc manual checks, which can be done in a simple Online-User-Interface bypassing the need for programming knowledge. We perform a thorough manual evaluation of the proposed methodology enabling us to show statistically that using automatic WSI increases the odds of finding non-recorded senses compared to a random selection of words. We further (i) evaluate predictions according to potential inclusion in the dictionary providing strong evidence for usefulness in practical lexicography, and (ii) analyze model predictions in-depth to point towards future improvements. We, finally, integrate lessons learned from our analysis into a large-scale prediction effort, providing the first high-quality large-scale WSI predictions for Swedish. These are a valuable resource for future research in Swedish lexicography.
