Query Auto Completion for Math Formula Search
Shaurya Rohatgi, Wei Zhong, Richard Zanibbi, Jian Wu, C. Lee Giles

TL;DR
This paper explores query auto completion for mathematical formulas, implementing and evaluating five methods on a specialized dataset, revealing that Finite State Transducer performs best with a notable MRR score.
Contribution
It is the first comprehensive evaluation of QAC methods specifically for mathematical formulas using the NTCIR-12 dataset.
Findings
Finite State Transducer outperforms other models
MRR score of 0.642 achieved by the best method
Evaluation provides insights into efficiency of QAC for math formulas
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 .
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMathematics, Computing, and Information Processing · Advanced Database Systems and Queries · Statistics Education and Methodologies
