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The idea of using lexical translations to define sense inventories has a long history in lexical semantics. We propose a theoretical framework which allows us to answer the question of why this apparently reasonable idea failed to produce useful results. We formally prove several propositions on how the translations of a word relate to its senses, as well as on the relationship between synonymy and polysemy. We empirically validate our theoretical findings on BabelNet, and demonstrate how they could be used to perform unsupervised word sense disambiguation of a substantial fraction of the lexicon.
Word sense disambiguation (WSD) methods identify the most suitable meaning of a word with respect to the usage of that word in a specific context. Neural network-based WSD approaches rely on a sense-annotated corpus since they do not utilize lexical
We present ESPnet-ST, which is designed for the quick development of speech-to-speech translation systems in a single framework. ESPnet-ST is a new project inside end-to-end speech processing toolkit, ESPnet, which integrates or newly implements auto
Personalized chatbots focus on endowing chatbots with a consistent personality to behave like real users, give more informative responses, and further act as personal assistants. Existing personalized approaches tried to incorporate several text desc
In this paper, we are going to focus on speed up of the Word Sense Disambiguation procedure by filtering the relevant senses of an ambiguous word through Part-of-Speech Tagging. First, this proposed approach performs the Part-of-Speech Tagging operat
Word senses are not static and may have temporal, spatial or corpus-specific scopes. Identifying such scopes might benefit the existing WSD systems largely. In this paper, while studying corpus specific word senses, we adapt three existing predominan