數學式可以用不同的符號或語句順序表達出同樣意義的式子，因此數學式檢索與一般的文字檢索有不同挑戰。本論文的研究目標是在大量的數學式中檢索與目標數學式相似的數學式。採用自監督圖神經網路對比學習方法，在NTCIR-12資料集上進行數學式檢索任務，並以nDCG及bpref作為評估指標。為了獲取更好的表現，本論文利用Tangent-CFT的嵌入作為圖模型預訓練特徵。當不考慮數學式上下文時，圖模型使用這些預訓練特徵在NTCIR-12資料集上取得了最佳的表現結果。;One mathematical formula can be expressed using different symbols or sequences. Therefore, retrieving mathematical expressions poses unique challenges compared to general text retrieval. This paper aims to retrieve mathematical formulas similar to target formula from a large collection of mathematical formulas. We adopt graph neural with self-supervised contrastive learning approached to tackle the task. We utilize the pre-trained embedding learned from Tangent-CFT as the features for the nodes and edges in graph. We evaluate the performance using the NTCIR-12 dataset with nDCG and bpref as evaluation metric. The graph neural networks using these pretraining embeddings perform best on the NTCIR-12 dataset.
