MadDog: A Web-based System for Acronym Identification and Disambiguation

Abstract

Acronyms and abbreviations are the shortform of longer phrases and they are ubiquitously employed in various types of writing. Despite their usefulness to save space in writing and time in reading, they also provide challenges for understanding the text especially if the acronym is not defined in the text or if it is used far from its definition in long texts. To alleviate this issue, there are considerable efforts both from the research community and software developers to build systems for identifying acronyms and finding their correct meanings in the text. However, none of the existing works provide a unified solution capable of processing acronyms in various domains and to be publicly available. Thus, we introduce MadDog, the first web-based acronym identification and disambiguation system which can process acronyms from various domains including scientific, biomedical, and general domains. The web-based system is publicly available at http://iq.cs.uoregon.edu: 5000 and a demo video is available at https: //youtu.be/IkSh7LqI42M.

GLADIS: A General and Large Acronym Disambiguation Benchmark

“…Early work observed that acronyms and their long forms appear frequently together in a document, as in "Artificial Intelligence (AI)". Based on this pattern, many approaches identify and extract acronyms by using rules (Yeates et al, 2000;Larkey et al, 2000;Pustejovsky et al, 2001;Park and Byrd, 2001;Yu et al, 2002;Schwartz and Hearst, 2002;Adar, 2004;Ao and Takagi, 2005;Okazaki and Ananiadou, 2006;Sohn et al, 2008;Veyseh et al, 2021) or supervised methods (Chang et al, 2002;Nadeau and Turney, 2005;Kuo et al, 2009;Movshovitz-Attias and Cohen, 2012;Liu et al, 2017;Wu et al, 2017;. In our work, we build on previous work (Schwartz and Hearst, 2002) for Acronym Identification, and focus mainly on disambiguation.…”

“…• MadDog (Veyseh et al, 2021) is a web-based acronym disambiguation system for multiple domains. It first creates chunks in which all samples with the same acronyms are assigned to the same chunks.…”

“…Besides, SciAD suffers from the problem of data leakage, because the train and test sets have overlapping pairs of acronym and long form. Finally, current general AD systems such as MadDog (Veyseh et al, 2021) rely on static word embeddings and LSTMs (Long Short Term Memory (Hochreiter and Schmidhuber, 1997)). Thus, they do not leverage pre-training on large corpora, which drives the current state of the art in most NLP tasks with contextual embeddings like BERT (Devlin et al, 2018).…”

Digitizing Receipts with OCR

“…Although aimed for text-dense documents, the acronym disambiguation approach proposed by [36] has potential. A dictionary is created by scraping multiple websites which are dense in acronyms and their corresponding long forms (e.g.…”

“…Because AD is context-dependent, there is no definitive solution proposed that can be directly implemented into this application. Despite that, the web-scraping approach by [36] is modified to be specialized for receipt item names. Since AD in receipts is only used in the search function, the disambiguated words are not visible or displayed to the user.…”