Contextualized Formula Search Using Math Abstract Meaning Representation
Behrooz Mansouri, Douglas W. Oard, Richard Zanibbi · Proceedings of the 31st ACM International Conference on Information & Knowledge Management · 2022
In math formula search, relevance is determined not only by the similarity of formulas in isolation, but also by their surrounding context. We introduce MathAMR, a new unified representation for sentences containing math. MathAMR generalizes Abstract Meaning Representation (AMR) graphs to include math formula operations and arguments. We then use Sentence-BERT to embed linearized MathAMR graphs for use in formula retrieval. In our first experiment, we compare MathAMR against raw text using the same formula representation (Operator Trees), and find that MathAMR produces more effective rankings. We then apply our MathAMR embeddings to reranking runs from the ARQMath-2 formula retrieval task, where in most cases effectiveness measures are improved. The strongest reranked run matches the best P$'[email protected] for an original run, and exceeds the original runs in nDCG$'[email protected]