| 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. |
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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. |
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. |
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. |
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 . |
| Outcome: | The proposed framework can be deployed to any enterprise to support acronym disambiguation. |
Better Word Embeddings by Disentangling Contextual n-Gram Information (N19-1)
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| Challenge: | Pre-trained word vectors are ubiquitous in Natural Language Processing applications. |
| Approach: | They show that word embeddings with bigram and trigram embedds improve unigram embeds . they claim this removes contextual information from unigrammes, resulting in better unigraph embedders . |
| Outcome: | The proposed model outperforms competing models on a wide variety of tasks. |
Enhancing the Context Representation in Similarity-based Word Sense Disambiguation (2021.emnlp-main)
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| Challenge: | Existing similarity-based systems focus on learning sense embeddings using only the sentence where the word appears, neglecting its global context. |
| Approach: | They propose a contextoriented embedding technique that takes better advantage of both word-level and sense-level global context of an ambiguous word for disambiguation. |
| Outcome: | The proposed method improves on all-words WSD benchmarks in knowledge-based category by large margins. |
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 . |
Improved Word Sense Disambiguation Using Pre-Trained Contextualized Word Representations (D19-1)
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| Challenge: | Contextualized word representations are effective in downstream tasks such as question answering, named entity recognition, and sentiment analysis. |
| Approach: | They propose to integrate pre-trained contextualized word representations into a neural network that captures the whole sentence and the word representation in the sentence. |
| Outcome: | The proposed approach outperforms the state-of-the-art approach that makes use of non-contextualized word embeddings on multiple benchmark WSD datasets. |
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 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. |