Complex Mathematical Symbol Definition Structures: A Dataset and Model
  for Coordination Resolution in Definition Extraction
Anna Martin-Boyle Andrew Head Kyle Lo Risham Sidhu Marti A. Hearst Dongyeop Kang
Mathematical symbol definition extraction is important for improving
scholarly reading interfaces and scholarly information extraction (IE).
However, the task poses several challenges: math symbols are difficult to
process as they are not composed of natural language morphemes; and scholarly
papers often contain sentences that require resolving complex coordinate
structures. We present SymDef, an English language dataset of 5,927 sentences
from full-text scientific papers where each sentence is annotated with all
mathematical symbols linked with their corresponding definitions. This dataset
focuses specifically on complex coordination structures such as "respectively"
constructions, which often contain overlapping definition spans. We also
introduce a new definition extraction method that masks mathematical symbols,
creates a copy of each sentence for each symbol, specifies a target symbol, and
predicts its corresponding definition spans using slot filling. Our experiments
show that our definition extraction model significantly outperforms RoBERTa and
other strong IE baseline systems by 10.9 points with a macro F1 score of 84.82.
With our dataset and model, we can detect complex definitions in scholarly
documents to make scientific writing more readable.