MCAT Math Retrieval System for NTCIR-12 MathIR Task

This paper describes the participation of our MCAT search system in the NTCIR-12 MathIR Task. We introduce three granularity levels of textual information, new approach for generating dependency graph of math expressions, score normalization, cold-start weights, and unification. We find that these modules, except the cold-start weights, have a very good impact on the search performance of our system. The use of dependency graph significantly improves precision of our system, i.e., up to 24.52% and 104.20% relative improvements in the Main and Simto subtasks of the arXiv task, respectively. In addition, the implementation of unification delivers up to 2.90% and 57.14% precision improvements in the Main and Simto subtasks, respectively. Overall, our best submission achieves P@5 of 0.5448 in the Main subtask and 0.5500 in the Simto subtask. In the Wikipedia task, our system also performs well at the MathWikiFormula subtask. At the MathWiki subtask, however, due to a problem with handling queries formed as questions that contain many stop words, our system finishes second.