| 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. |
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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. |
| Outcome: | The proposed method can recover normalized, expanded abbreviations from text . it is similar to spelling correction, but requires more extensive work . |
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 . |
| 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. |
Expanding Abbreviations in a Strongly Inflected Language: Are Morphosyntactic Tags Sufficient? (L18-1)
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| Challenge: | In this paper, the problem of recovery of morphological information lost in abbreviated forms is addressed . correct inflected form of expanded abbrevation can be deduced from context words . |
| Approach: | They propose a deep bidirectional LSTM network with tag embedding to predict abbreviated words . they train on 10 million words from the Polish Sejm Corpus and achieve 74.2% prediction accuracy . |
| Outcome: | The proposed model achieves 74.2% accuracy on a smaller but more general corpus of Polish words. |
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. |
A Short Survey on Sense-Annotated Corpora (2020.lrec-1)
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| Challenge: | Word Sense Disambiguation (WSD) is a key task in Natural Language Understanding. |
| Approach: | They propose to use sense-annotated corpora for supervised Word Sense Disambiguation. |
| Outcome: | The proposed methods have been compared with knowledge-based approaches and have shown to be more efficient when they are available. |
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. |
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 . |
| Outcome: | The proposed method outperforms (classical) cosine similarity in a set of scientific papers. |
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. |
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 . |
| Approach: | They propose to annotate 27,200 sentences in 6 different languages and 2 new domains for AE. |
| Outcome: | The proposed dataset shows that AE in different languages and learning settings has unique challenges . |
Annotation and Automatic Classification of Aspectual Categories (P19-1)
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| Challenge: | Annotated resource for aspectual classification of German verb tokens in context. |
| Approach: | They present a resource for aspectual classification of German verb tokens in their clausal context. |
| Outcome: | The proposed resource is compared with previous work on German verb tokens using aspectual features compatible with the plurality of aspectual classifications. |