Query Auto Completion for Math Formula Search
This work addresses the need for efficient query formulation in math formula search, but it is incremental as it applies existing methods to a new domain.
The paper tackled the problem of query auto-completion for mathematical formulas, an area with limited prior work, by implementing and evaluating five existing QAC methods on the NTCIR-12 MathIR dataset, finding that the Finite State Transducer model achieved the best performance with an MRR score of 0.642.
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 $0.642$.