Query Auto Completion for Math Formula Search

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 $0.642$.