Mathematical formulas are commonly used to demonstrate theories and basic fundamentals
in the Science, Technology, Engineering, and Mathematics (STEM) domain. The burgeoning …

When searching for mathematical content, accurate measures of formula similarity can help
with tasks such as document ranking, query recommendation, and result set clustering …

Automating discovery in mathematics and science will require sophisticated methods of
information extraction and abstract reasoning, including models that can convincingly …

With the recent success of dense retrieval methods based on bi-encoders, studies have
applied this approach to various interesting downstream retrieval tasks with good efficiency …

Math-aware search engines need to support formulae in queries. Mathematical expressions
are typically represented as trees defining their operational semantics or visual layout. We …

Formula retrieval systems using substructure matching are effective, but suffer from slow
retrieval times caused by the complexity of structure matching. We present a specialized …

We present a new search method for mathematical formulas based on Operator Trees
(OPTs) representing the application of operators to operands. Our method provides (1) a …

Mathematical information is essential for technical work, but its creation, interpretation, and
search are challenging. To help address these challenges, researchers have developed …

Word embedding, which represents individual words with semantically fixed-length vectors,
has made it possible to successfully apply deep learning to natural language processing …

Neural retrievers have been shown to be effective for math-aware search. Their ability to
cope with math symbol mismatches, to represent highly contextualized semantics, and to …