Addressing polysemy in NLP tasks Looking for modern algorithms using NN Language Model implementations addressing polysemy in NLP tasks, including text classification, question answering and topic modeling. Transfer/Zero-short learning methods are most interesting to find. Any working solutions with BERT and Hugging Face Transformers libraries?
Maybe this article will help you How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings.
Talks about contextual word embeddings like BERT and GPT how they can capture various polysemous concepts rather than the static word embeddings which create a single representation for each word, such as GloVe.