Multi-dimensional Formula Feature Modeling for Mathematical Information Retrieval

With the quick availability and growth of mathematical information worldwide, how to effectively retrieve the relevant information about mathematical formulae, has attracted increasing attention from the researchers of mathematical information retrieval (MIR). Existing methods mainly focus on the appearance similarity between formulae. However, there are more important formula-related information that could be explored, for instance, link relations between formulae, formula contexts and temporal information. In this study, I propose a novel formula feature modeling method for mathematical information retrieval. In more details, three new formula features have been proposed for better representing mathematical formulae: formula-related concept features extracted from link structure (Formula Citation Graph, FCG), essential semantic features extracted from descriptive textual information of formulae through Recurrent Neural Networks (RNN) and temporal features extracted from time-related information. All these features could be used to index and retrieve formulae.