Challenge: Existing abbreviation expansion systems or tools require technical knowledge to set up . existing systems require strong assumptions and are limited in their usefulness .
Approach: They propose a web-based system that automatically expands abbreviations and acronyms in a user provided document.
Outcome: The proposed system expands abbreviations and acronyms automatically in a user provided document.

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MadDog: A Web-based System for Acronym Identification and Disambiguation (2021.eacl-demos)

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Challenge: Acronyms and abbreviations are the short-form of longer phrases and are frequently used in writing but they can also present challenges for newcomers.
Approach: They propose to develop a web-based acronym identification and disambiguation system which can process acronyms from various domains including scientific, biomedical, and general domains.
Outcome: The proposed system can process acronyms from scientific, biomedical, and general domains.
Abbreviation Explorer - an interactive system for pre-evaluation of Unsupervised Abbreviation Disambiguation (N19-4)

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Challenge: Abbreviation Explorer helps to identify long-forms that are easily confused . it can also pinpoint likely causes such as limitations of normalization, language switching, or inconsistent typing.
Approach: They propose a system that supports interactive exploration of abbreviations that are challenging for Unsupervised Abbreviation Disambiguation.
Outcome: The proposed system can identify long-forms that are easily confused and pinpoint likely causes . it can also identify which long-terms would benefit from additional input text . the proposed rules can be easily applied to existing vector spaces to improve performance while avoiding the cost of retraining.
Structured abbreviation expansion in context (2021.findings-emnlp)

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Challenge: Ad hoc abbreviations are commonly found in informal communication channels that favor shorter messages.
Approach: They propose to reverse ad hoc abbreviations in context to recover normalized, expanded versions of abbrevated messages.
Outcome: The proposed method can recover normalized, expanded abbreviations from text . it is similar to spelling correction, but requires more extensive work .
What Does This Acronym Mean? Introducing a New Dataset for Acronym Identification and Disambiguation (2020.coling-main)

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Challenge: Acronyms are short forms of phrases that facilitate conveying lengthy sentences in documents.
Approach: They propose to annotate a large dataset for scientific domain and a new deep learning model which expands an ambiguous acronym in a sentence.
Outcome: The proposed model outperforms the state-of-the-art models on the new dataset.
Experiments with ad hoc ambiguous abbreviation expansion (D19-62)

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Challenge: ad hoc abbreviations are difficult to interpret for patients and nonspecialists.
Approach: They propose to use morphologically annotated medical notes to expand ad hoc abbreviations without using additional domain resources.
Outcome: The proposed methods outperform the previously proposed methods on Polish data but can be used for other languages.
GLADIS: A General and Large Acronym Disambiguation Benchmark (2023.eacl-main)

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Challenge: Existing acronym disambiguation benchmarks are limited to specific domains . a study on a Microsoft question answering forum found that only 7% of acronyms co-occur with their corresponding long forms, which confuses the readers about the meaning of a text.
Approach: They propose a new acronym disambiguation benchmark with a dictionary and a pre-training corpus . they then pre-train a language model on the constructed corpus and show the challenges .
Outcome: The proposed benchmarks pre-train a language model on the constructed corpus for general acronym disambiguation.
Guess Me if You Can: Acronym Disambiguation for Enterprises (P18-1)

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Challenge: Acronyms are abbreviations formed from the initial components of words or phrases . acronyms can be difficult to understand for people who are not familiar with the subject matter .
Approach: They propose a framework to automatically resolve the true meanings of acronyms in a given context . they use the enterprise corpus as input and a high-quality acronym disambiguation system as output .
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MACRONYM: A Large-Scale Dataset for Multilingual and Multi-Domain Acronym Extraction (2022.coling-1)

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Challenge: Acronym extraction is the task of identifying acronyms and their expanded forms in texts . existing AE methods for English are limited to specific languages and domains .
Approach: They propose to annotate 27,200 sentences in 6 different languages and 2 new domains for AE.
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PLOD: An Abbreviation Detection Dataset for Scientific Documents (2022.lrec-1)

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Challenge: Existing datasets for abbreviation detection and extraction are limited.
Approach: They propose to use a large-scale dataset for abbreviation detection and extraction that contains 160k+ segments automatically annotated with abbrevian and long forms.
Outcome: The proposed dataset has an F1 score of 0.92 for abbreviations and 0.89 for detecting their corresponding long forms.
A Chinese Dataset with Negative Full Forms for General Abbreviation Prediction (L18-1)

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Challenge: a common phenomenon across languages is abbreviation, but it's not always possible to predict it accurately.
Approach: They build a dataset for general Chinese abbreviation prediction using a negative full form . they find that abbrevation prediction can improve the performance of abbreviation recognition .
Outcome: The proposed dataset evaluates models on abbreviation prediction in Chinese . it shows that abbrevation prediction improves performance in language processing tasks .

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