LSTM Neural Network Based Math Information Retrieval

The work presented in this paper ascertains role of Long Sort-Term Memory (LSTM) neural network in Math Information Retrieval (MIR). Motivated from promising performances of the LSTM for sequence-to-sequence tasks, an LSTM based Formula Entailment (LFE) module is implemented for recognizing entailment between mathematical user query and document formulae. The LFE module is trained and validated using a symbol level Math Formula Entailment (MENTAIL) dataset. The relevance of a document is determined by the fraction of document formulae which entail the user query. A reasonable score of 0.45 for the P_5 evaluation measure substantiates competence of the implemented MIR system in retrieving relevant documents corresponding to a mathematical user query.