A hybrid model for finding abbreviation–definition pairs from biomedical abstracts using heuristics-based sequence labeling and perceptron linear classifier

Menaha, R.; Jayanthi, V. E.

doi:10.1016/j.eswa.2020.114049

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…A hybrid approach does acronym expansion detection in normal text data by using POS Tagging, latent semantic analysis, and deep learning model (Chowdhury et al, 2019). A hybrid model is developed for recognizing acronym expansion pairs from biomedical text (Menaha & Jayanthi, 2020). Three mapping strategies such as predecessor level mapping, character level mapping, and word level mapping are used for recognizing the candidate acronym expansion sequence, and the perceptron model is used for validating the retrieved acronym expansion sequence.…”

Section: Related Workmentioning

confidence: 99%

Heuristics‐based sequence labelling model for finding educational domain acronym expansions

Menaha¹,

Jayanthi

2022

Expert Systems

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An acronym is a short form of the text used in many applications like Web search, Research and scientific documents, information retrieval, biomedical documents, etc.The main objective of the work is to develop a heuristics-based sequence labelling model to identify acronym expansion pairs in educational documents. The proposed system is implemented in four phases: (i) Data Collection (ii) Acronym Sequence Identification (iii) Expansion Determination and (iv) Acronym Expansion Validation. In this work, the acronym sequences identification is done by heuristic rules. Determining expansions is accomplished by a heuristics-based sequence labelling model. Again, the validation of acronym expansion sequences is also done by using heuristics rules.The result section shows that our proposed heuristics-based sequence labelling model achieved 98.4% accuracy in finding acronym expansion pairs from education research documents. The results are also compared with the acronym finder online acronym expansion repository and proved that more new acronym expansion sequences are identified by using our model.

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Section: Related Workmentioning

confidence: 99%

Heuristics‐based sequence labelling model for finding educational domain acronym expansions

Menaha¹,

Jayanthi

2022

Expert Systems

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“…In the last two decades, several research works are under practice to automate the recognition of acronym‐expansion pairs from text and web documents. In which, several research works are centered on recognizing acronym‐definition pair from biomedical text documents 2,3 . and plain text documents 4,5,6 .…”

Section: Introductionmentioning

confidence: 99%

Mining the web to discover acronym‐definitions based on sequence labeling and iterative query expansion model

Menaha¹,

Jayanthi

2021

Concurrency and Computation

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Summary Finding definitions associated with an acronym is a constructive task to many applications like web search, information retrieval, natural language processing, ontology mapping, and question answering. A large‐scale manually built acronym‐definition repositories are available online but updating is a difficult task. To address this problem, previous research works used either heuristics or a machine learning approach to automate the detection of acronym‐definition pairs. This article presents a heuristics approach based on a rule‐based sequence‐labeling model, for finding the list of definitions associated with an acronym from the Web. The organic search result of the web page includes website title, URL, Meta description, and site links. In the proposed work, web pages of the search engine are used as corpus and sequence‐labeling task is done through character and word level mapping schemes. In these schemes, the desirable properties of the definition are expressed by using rules. Each identified definition is validated through a collocation measure. Besides, the obtained results are assessed against a manually built acronym/definitions repository: Acronym Finder. The proposed model certainly improves the coverage of manual repositories maintaining high precision and recall.

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Disambiguation of medical abbreviations for knowledge organization

Wang

et al. 2023

Information Processing & Management

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A hybrid model for finding abbreviation–definition pairs from biomedical abstracts using heuristics-based sequence labeling and perceptron linear classifier

Cited by 3 publications

References 11 publications

Heuristics‐based sequence labelling model for finding educational domain acronym expansions

Heuristics‐based sequence labelling model for finding educational domain acronym expansions

Mining the web to discover acronym‐definitions based on sequence labeling and iterative query expansion model

Disambiguation of medical abbreviations for knowledge organization

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