Papers by Xiaohang Tang
Can Word Sense Distribution Detect Semantic Changes of Words? (2023.findings-emnlp)
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| Challenge: | Existing methods to detect semantic variations of words are not accurate for time-sensitive predictions. |
| Approach: | They propose to use pretrained static sense embeddings to annotate a word's occurrence with a sense id to compare its distributions. |
| Outcome: | The proposed method compares word sense distributions across two corpora to predict meaning change . the results show that pretrained LLMs can detect changes in words over time . |
Learning Dynamic Contextualised Word Embeddings via Template-based Temporal Adaptation (2023.acl-long)
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| Challenge: | Existing methods for learning dynamic contextualised word embeddings do not capture temporal semantic variations of words. |
| Approach: | They propose a method for learning DCWEs by time-adapting a pretrained Masked Language Model using time-sensitive templates. |
| Outcome: | The proposed method significantly reduces the perplexity of test sentences in C2 outperforming the current state-of-the-art. |