Proceedings of the 18th Conference on Computer Science and Intelligence Systems

DOI: http://dx.doi.org/10.15439/2023F839

Citation: Proceedings of the 18th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 35, pages 1255–1264 (2023)

Full text

Abstract. The disambiguation of abbreviations and acronyms is a longstanding problem in Natural Language Processing (NLP) that has garnered significant attention from researchers. Previous approaches have employed statistical methods, semantic similarity metrics, and machine learning algorithms. Various languages and document types have been explored, with English being the most commonly studied language. This paper presents a comprehensive review of research efforts in abbreviation disambiguation, encompassing different languages, document types, and methodologies employed. Recent studies have showcased the effectiveness of pre-trained encoder-decoder models, while the emergence of multilingual models has enabled tackling abbreviation disambiguation across multiple languages. Standardization and addressing the challenges of multilingual and multi-document type disambiguation remain ongoing goals in the field of NLP. This paper provides valuable insights into the current state-of-the-art approaches and identifies future research directions. The methods are evaluated in the context of the PolEval abbreviation disambiguation competition, where the authors achieve top ranking.

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