Papers with representing fine-grained word senses

1 papers
Diachronic Sense Modeling with Deep Contextualized Word Embeddings: An Ecological View (P19-1)

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Challenge: Existing word embeddings only assign one vector to a word for a time period, thus they face the meaning conflation deficiency.
Approach: They propose a sense representation and tracking framework based on deep contextualized embeddings that can be used to answer what and when the word meaning changes.
Outcome: The proposed framework is effective in representing fine-grained word senses, and brings a significant improvement in word change detection task.

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