Papers with sense representations
Which Evaluations Uncover Sense Representations that Actually Make Sense? (2020.lrec-1)
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| Challenge: | Existing sense representations fail for human-centric tasks like inspecting a language’s sense inventory. |
| Approach: | They propose a coherence evaluation for sense embeddings and a model optimized for finding interpretable sense representations that are more coherent than existing sense embeds. |
| Outcome: | The proposed model is more coherent than existing sense embeddings and offers comparable word similarities with multisense representations while learning more distinguishable, interpretable senses. |
With More Contexts Comes Better Performance: Contextualized Sense Embeddings for All-Round Word Sense Disambiguation (2020.emnlp-main)
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| Challenge: | Contextualized word embeddings have been used effectively across several tasks in Natural Language Processing, but it is difficult to link them to structured sources of knowledge. |
| Approach: | They propose a semi-supervised approach to producing sense embeddings for the lexical meanings within a lexicon that is comparable to that of contextualized word vectors. |
| Outcome: | The proposed approach outperforms state-of-the-art models in the English Word Sense Disambiguation task and in the multilingual one while training on sense-annotated data in English only. |
Improved Word Sense Disambiguation with Enhanced Sense Representations (2021.findings-emnlp)
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| Challenge: | Existing supervised word sense disambiguation systems do not provide enough information about word senses. |
| Approach: | They propose to incorporate synonyms, example phrases or sentences showing usage of word senses and sense gloss of hypernyms into the sense representations. |
| Outcome: | The proposed system achieves an F1 score of 82.0% on the standard benchmark test dataset of the English all-words WSD task. |
GGP: Glossary Guided Post-processing for Word Embedding Learning (2020.lrec-1)
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| Challenge: | Existing word embedding models require much training time and domain knowledge to improve. |
| Approach: | They propose a GGP-based word embedding model that incorporates the glossary and learns sense representations. |
| Outcome: | The proposed model outperforms existing models on topical/functional similarity datasets by 4.1% and 7%. |