Acquiring large amounts of high-quality annotated data is an open issue in word sense disambiguation.This problem has become more critical recently with the advent of supervised models based on neural networks, which require large amounts of annotated data.We propose two algorithms for making selective corrections on a sense-annotated parallel corpus, based on cross-lingual synset mappings.We show that, when applied to bilingual parallel corpora, these algorithms can rectify noisy sense annotations, and thereby produce multilingual sense-annotated data of improved quality.

Colexification occurs when two distinct concepts are lexified by the same word.The term covers both polysemy and homonymy.We posit and investigate the hypothesis that no pair of concepts are colexified in every language.We test our hypothesis by analyzing colexification data from BabelNet, Open Multilingual WordNet, and CLICS.The results show that our hypothesis is supported by over 99.9% of colexified concept pairs in these three lexical resources.

Deciding whether a semantically ambiguous word is homonymous or polysemous is equivalent to establishing whether it has any pair of senses that are semantically unrelated.We present novel methods for this task that leverage information from multilingual lexical resources.We formally prove the theoretical properties that provide the foundation for our methods.In particular, we show how the One Homonym Per Translation hypothesis of Hauer and Kondrak (2020a) follows from the synset properties formulated by Hauer and Kondrak (2020b).Experimental evaluation shows that our approach sets a new state of the art for homonymy detection.