Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/468676
Title: Hybrid system for recognizing Acronym expansions using heuristics And machine learning technique
Researcher: Menaha, R
Guide(s): Jayanthi, VE
Keywords: Engineering and Technology
Engineering
Engineering Electrical and Electronic
Data Mining
Neural Network
Biomedical Abstract
University: Anna University
Completed Date: 2021
Abstract: An acronym is a type of abbreviation made up of initial letter or letters of other words. An abbreviation is a short form (SF) of a phrase. The long-form (LF) of an abbreviation is called either a definition or an expansion. Abbreviations and acronyms are commonly used in biomedical literature, scientific and technical articles, information retrieval and web search, etc. Recognizing full forms that are associated with the acronym is important for identifying the meaning of an acronym that facilitates natural language processing and information retrieval from the literature. newlineSeveral research works are under practice to automate the recognition of acronym expansion pairs from text and web documents. Heuristics or Machine Learning approaches are prevalently pursued extracting acronym-definition from text or web. Existing heuristics and machine learning approaches recall rate (i.e. Number of retrieved acronym expansion pairs from document rate) is low. Hence, a hybrid model combining heuristics and machine learning is proposed in this work to retrieve more number of acronym expansion pairs from documents. The main objective of the work is to extract abbreviation definition pairs from text documents and also find the list of definitions of the acronym from the web. newlineFirstly, seven space reduction heuristics are applied to recognize acronyms from the text. Then, three mapping strategies are proposed for doing a sequence labeling task to recognize the expansion of the acronym. Since the usage of acronyms is more in biomedical literature, a biomedical dataset is created from Thalia semantic search engine. Then, the dataset is utilized to identify the potential abbreviation definition pairs. newline
Pagination: xv,115p.
URI: http://hdl.handle.net/10603/468676
Appears in Departments:Faculty of Information and Communication Engineering

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01_title.pdfAttached File29.48 kBAdobe PDFView/Open
02_prelim pages.pdf3.04 MBAdobe PDFView/Open
03_content.pdf18.56 kBAdobe PDFView/Open
04_abstract.pdf13.06 kBAdobe PDFView/Open
05_chapter 1.pdf198.81 kBAdobe PDFView/Open
06_chapter 2.pdf81.35 kBAdobe PDFView/Open
07_chapter 3.pdf202.83 kBAdobe PDFView/Open
08_chapter 4.pdf100.07 kBAdobe PDFView/Open
09_chapter 5.pdf334.06 kBAdobe PDFView/Open
10_chapter 6.pdf366.74 kBAdobe PDFView/Open
11_annexures.pdf79.76 kBAdobe PDFView/Open
80_recommendation.pdf82.14 kBAdobe PDFView/Open
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