Pascal Denis, Mikaela Keller, Bastien Liétard
We lifted 4 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| blietard/concept-induction | canonical | 2 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| get_algo_class | Ran | blietard/concept-induction/utilities/clustering.py code served (permissive licence) · get_code("452f936a6bdf7b80") |
| verif_layers_arg | Ran | blietard/concept-induction/utilities/languagemodel.py code served (permissive licence) · get_code("3e405281884794dc") |
| get_tokenizer | Not yet run | blietard/concept-induction/utilities/languagemodel.py code served (permissive licence) · get_code("81b99eca69445465") |
| initialize_models | Not yet run | blietard/concept-induction/utilities/languagemodel.py code served (permissive licence) · get_code("e29e021d7ad378d7") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Polysemy and synonymy are two crucial interrelated facets of lexical ambiguity. While both phenomena are widely documented in lexical resources and have been studied extensively in NLP, leading to dedicated systems, they are often being considered independently in practictal problems. While many tasks dealing with polysemy (e.g. Word Sense Disambiguiation or Induction) highlight the role of word's senses, the study of synonymy is rooted in the study of concepts, i.e. meanings shared across the lexicon. In this paper, we introduce Concept Induction, the unsupervised task of learning a soft clustering among words that defines a set of concepts directly from data. This task generalizes Word Sense Induction. We propose a bi-level approach to Concept Induction that leverages both a local lemma-centric view and a global cross-lexicon view to induce concepts. We evaluate the obtained clustering on SemCor's annotated data and obtain good performance (BCubed F 1 above 0.60). We find that the local and the global levels are mutually beneficial to induce concepts and also senses in our setting. Finally, we create static embeddings representing our induced concepts and use them on the Word-in-Context task, obtaining competitive performance with the State-ofthe-Art.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2406.20054")
get_code_for_paper("2406.20054")
have("2406.20054")
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