Extracting semantic information on measurements and counts is an important
topic in terms of analyzing scientific discourses. The 8th task of
SemEval-2021: Counts and Measurements (MeasEval) aimed to boost research in
this direction by providing a new dataset on which participants train their
models to extract meaningful information on measurements from scientific texts.
The competition is composed of five subtasks that build on top of each other:
(1) quantity span identification, (2) unit extraction from the identified
quantities and their value modifier classification, (3) span identification for
measured entities and measured properties, (4) qualifier span identification,
and (5) relation extraction between the identified quantities, measured
entities, measured properties, and qualifiers. We approached these challenges
by first identifying the quantities, extracting their units of measurement,
classifying them with corresponding modifiers, and afterwards using them to
jointly solve the last three subtasks in a multi-turn question answering
manner. Our best performing model obtained an overlapping F1-score of 36.91% on
the test set.

该研究旨在通过语义信息提取对科学文献中的计量和计数进行分析，并提供数据集以培训模型提取科学文本中的测量信息，并通过五个子任务来构建，并采用多回合问答的方法解决前三个子任务，最终在测试集上获得了 36.91％的重叠 F1 分数。