TartuNLP @ AXOLOTL-24: Leveraging Classifier Output for New Sense Detection in Lexical Semantics
This work addresses the challenge of lexical semantic change detection for computational linguistics, but it is incremental as it builds on existing methods for a shared task.
The paper tackled the problem of detecting new senses that words gain over time and generating definitions for them, achieving third place on the detection subtask and first place on the definition subtask in the AXOLOTL-24 shared task.
We present our submission to the AXOLOTL-24 shared task. The shared task comprises two subtasks: identifying new senses that words gain with time (when comparing newer and older time periods) and producing the definitions for the identified new senses. We implemented a conceptually simple and computationally inexpensive solution to both subtasks. We trained adapter-based binary classification models to match glosses with usage examples and leveraged the probability output of the models to identify novel senses. The same models were used to match examples of novel sense usages with Wiktionary definitions. Our submission attained third place on the first subtask and the first place on the second subtask.