Acronym Identification and Disambiguation Shared Tasks for Scientific Document Understanding
This work addresses the problem of accurately identifying and disambiguating acronyms in scientific documents, which is crucial for improving text understanding tools for researchers and practitioners in various scientific fields.
The authors organized two shared tasks, AI@SDU and AD@SDU, for acronym identification and disambiguation in scientific documents to address limitations of prior work in the biomedical domain and with limited datasets. These tasks attracted 52 and 43 participants respectively, with submitted systems showing substantial improvements over baselines but still falling short of human-level performance.
Acronyms are the short forms of longer phrases and they are frequently used in writing, especially scholarly writing, to save space and facilitate the communication of information. As such, every text understanding tool should be capable of recognizing acronyms in text (i.e., acronym identification) and also finding their correct meaning (i.e., acronym disambiguation). As most of the prior works on these tasks are restricted to the biomedical domain and use unsupervised methods or models trained on limited datasets, they fail to perform well for scientific document understanding. To push forward research in this direction, we have organized two shared task for acronym identification and acronym disambiguation in scientific documents, named AI@SDU and AD@SDU, respectively. The two shared tasks have attracted 52 and 43 participants, respectively. While the submitted systems make substantial improvements compared to the existing baselines, there are still far from the human-level performance. This paper reviews the two shared tasks and the prominent participating systems for each of them.