  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.
