  Acronyms are the short forms of phrases that facilitate conveying lengthy
sentences in documents and serve as one of the mainstays of writing. Due to
their importance, identifying acronyms and corresponding phrases (i.e., acronym
identification (AI)) and finding the correct meaning of each acronym (i.e.,
acronym disambiguation (AD)) are crucial for text understanding. Despite the
recent progress on this task, there are some limitations in the existing
datasets which hinder further improvement. More specifically, limited size of
manually annotated AI datasets or noises in the automatically created acronym
identification datasets obstruct designing advanced high-performing acronym
identification models. Moreover, the existing datasets are mostly limited to
the medical domain and ignore other domains. In order to address these two
limitations, we first create a manually annotated large AI dataset for
scientific domain. This dataset contains 17,506 sentences which is
substantially larger than previous scientific AI datasets. Next, we prepare an
AD dataset for scientific domain with 62,441 samples which is significantly
larger than the previous scientific AD dataset. Our experiments show that the
existing state-of-the-art models fall far behind human-level performance on
both datasets proposed by this work. In addition, we propose a new deep
learning model that utilizes the syntactical structure of the sentence to
expand an ambiguous acronym in a sentence. The proposed model outperforms the
state-of-the-art models on the new AD dataset, providing a strong baseline for
future research on this dataset.
