| 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 . |
| Outcome: | The proposed framework can be deployed to any enterprise to support acronym disambiguation. |
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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. |
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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. |
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. |
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Abbreviation Expander - a Web-based System for Easy Reading of Technical Documents (C18-2)
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| Challenge: | Existing abbreviation expansion systems or tools require technical knowledge to set up . existing systems require strong assumptions and are limited in their usefulness . |
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Using Word Embeddings for Unsupervised Acronym Disambiguation (C18-1)
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| Challenge: | Scientific papers contain many acronyms and abbreviations. |
| Approach: | They propose a method to choose the contextual correct definition of an acronym . they learn word embeddings for all words in the corpus and compare them with weighted averages . |
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Improving Entity Disambiguation by Reasoning over a Knowledge Base (2022.naacl-main)
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| Challenge: | Recent work in entity disambiguation relies on a limited subset of KB facts to link entities . less common entities are prone to missing or inconsistent KB information, which is problematic for models which rely on 'one source' |
| Approach: | They propose an ED model which links entities by reasoning over a symbolic knowledge base in a fully differentiable fashion. |
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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. |
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MACRONYM: A Large-Scale Dataset for Multilingual and Multi-Domain Acronym Extraction (2022.coling-1)
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Amir Pouran Ben Veyseh, Nicole Meister, Seunghyun Yoon, Rajiv Jain, Franck Dernoncourt, Thien Huu Nguyen
| 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 . |
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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. |
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ZELDA: A Comprehensive Benchmark for Supervised Entity Disambiguation (2023.eacl-main)
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| Challenge: | Entity disambiguation (ED) is the task of disambiguating named entity mentions in text to unique entries in a knowledge base. |
| Approach: | They propose a benchmark for entity disambiguation that includes a unified training data set, entity vocabulary, candidate lists and challenging evaluation splits covering 8 different domains. |
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