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Query Auto Completion for Math Formula Search

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arxiv 1912.04115 v1 pith:TFHUMC5U submitted 2019-12-09 cs.IR

Shaurya Rohatgi , Wei Zhong , Richard Zanibbi , Jian Wu , C. Lee Giles This is my paper · ORCID
classification cs.IR
keywords autocompletionformulaeffortmathematicalmeanquerysearch
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abstract

Query Auto Completion (QAC) is among the most appealing features of a web search engine. It helps users formulate queries quickly with less effort. Although there has been much effort in this area for text, to the best of our knowledge there is few work on mathematical formula auto completion. In this paper, we implement 5 existing QAC methods on mathematical formula and evaluate them on the NTCIR-12 MathIR task dataset. We report the efficiency of retrieved results using Mean Reciprocal Rank (MRR) and Mean Average Precision(MAP). Our study indicates that the Finite State Transducer outperforms other QAC models with a MRR score of $0.642$.

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  1. E-Gen: Leveraging E-Graphs to Improve Continuous Representations of Symbolic Expressions

    cs.LG 2025-01 conditional novelty 6.0 of 10

    An e-graph-based data generator produces 55 million equivalent-expression training pairs, and embeddings trained on them beat prior math-embedding models and GPT-4o on several symbolic math tasks.

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