Using surrounding text of formula towards more accurate mathematical information retrieval

Formula retrieval is an important research topic in Mathematical Information Retrieval (MIR). Most studies have focused on comparing formulae to determine the similarity between mathematical documents. However, two similar formulae may appear in completely different knowledge domains and have different meanings. Based on N-ary Tree-based Formula Embedding Model (NTFEM), we introduce a new hybrid retrieval model combining formula with its surrounding text for more accurate retrieval. Using keywords extraction technology, we extract keywords from text around the formula which can supplement the semantic information of formula. Then we get the representation vectors of keywords by FastText N-gram embedding model, and the representation vectors of formulae by NTFEM. Finally, documents are first sorted according to the similarity of keywords, and then the ranking results are optimized by formula similarity. Experimental results show that the accuracy of top-10 results is at least 20% higher than that of NTFEM and can be 50% in some specific topics.