In this work, we outline the components and results of our system submitted
to the AXOLOTL-24 shared task for Finnish, Russian and German languages. Our
system is fully unsupervised. It leverages a graph-based clustering approach to
predict mappings between unknown word usages and dictionary entries for Subtask
1, and generates dictionary-like definitions for those novel word usages
through the state-of-the-art Large Language Models such as GPT-4 and LLaMA-3
for Subtask 2. In Subtask 1, our system outperforms the baseline system by a
large margin, and it offers interpretability for the mapping results by
distinguishing between matched and unmatched (novel) word usages through our
graph-based clustering approach. Our system ranks first in Finnish and German,
and ranks second in Russian on the Subtask 2 test-phase leaderboard. These
results show the potential of our system in managing dictionary entries,
particularly for updating dictionaries to include novel sense entries. Our code
and data are made publicly
available\footnote{https://github.com/xiaohemaikoo/axolotl24-ABDN-NLP}.

本文描述了我们基于无监督系统的组成部分和结果，该系统使用基于图的聚类方法预测未知词语用法和字典条目之间的映射，并通过最先进的大型语言模型（如 GPT-4 和 LLaMA-3）为这些新颖词语用法生成类似字典的定义。我们的系统在 Subtask 1 中明显优于基准系统，在映射结果中通过图形聚类方法区分匹配和不匹配的（新颖）词语用法，提供了可解释性。我们的系统在芬兰语和德语中排名第一，在俄语中排名第二，展示了我们的系统在管理字典条目方面的潜力，特别是用于更新包含新意义条目的字典。