Towards the Detection and Formal Representation of Semantic Shifts in Inflectional Morphology

Semantic shifts caused by derivational morphemes is a common subject of investigation in language modeling, while inflectional morphemes are frequently portrayed as semantically more stable. This study is motivated by the previously established observation that inflectional morphemes can be just as variable as derivational ones. For instance, the English plural "-s" can turn the fabric silk into the garments of a jockey, silks. While humans know that silk in this sense has no plural, it takes more for machines to arrive at this conclusion. Frequently utilized computational language resources, such as WordNet, or models for representing computational lexicons, like OntoLex-Lemon, have no descriptive mechanism to represent such inflectional semantic shifts. To investigate this phenomenon, we extract word pairs of different grammatical number from WordNet that feature additional senses in the plural and evaluate their distribution in vector space, i.e., pre-trained word2vec and fastText embeddings. We then propose an extension of OntoLex-Lemon to accommodate this phenomenon that we call inflectional morpho-semantic variation to provide a formal representation accessible to algorithms, neural networks, and agents. While the exact scope of the problem is yet to be determined, this first dataset shows that it is not negligible.