Large-scale pre-trained models like BERT, have obtained a great success in various Natural
Language Processing (NLP) tasks, while it is still a challenge to adapt them to the math …

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 …

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

We present an overview of the NTCIR-12 MathIR Task, dedicated to information access for
mathematical content. The MathIR task makes use of two corpora. The first corpus contains …

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 …

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 …

Informal mathematical text underpins real-world quantitative reasoning and communication.
Developing sophisticated methods of retrieval and abstraction from this dual modality is …