Russian word sense induction by clustering averaged word embeddings
This is an incremental improvement for Russian NLP, addressing word sense disambiguation in a specific language context.
The authors tackled word sense induction for Russian by clustering averaged word embeddings, achieving 2nd place on a homonym dataset and 5th on polysemous datasets in a shared task. They also found that embeddings from small, balanced corpora can outperform those from large, noisy data in this task.
The paper reports our participation in the shared task on word sense induction and disambiguation for the Russian language (RUSSE-2018). Our team was ranked 2nd for the wiki-wiki dataset (containing mostly homonyms) and 5th for the bts-rnc and active-dict datasets (containing mostly polysemous words) among all 19 participants. The method we employed was extremely naive. It implied representing contexts of ambiguous words as averaged word embedding vectors, using off-the-shelf pre-trained distributional models. Then, these vector representations were clustered with mainstream clustering techniques, thus producing the groups corresponding to the ambiguous word senses. As a side result, we show that word embedding models trained on small but balanced corpora can be superior to those trained on large but noisy data - not only in intrinsic evaluation, but also in downstream tasks like word sense induction.