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Acronyms are present in usually all documents to express information that is repetitive and well known. But acronyms can be ambiguous because there can be many expansions of the same acronym. In this paper, we propose a general system for acronym expansion that can work on any acronym given some context information it is used in. We present methods for retrieving all the possible expansions of an acronym from Wikipedia and AcronymsFinder.com. We propose to use these expansions to collect the context in which these acronym expansions are used and then score them using a deep learning technique called Doc2Vec. All these things collectively lead to achieving an accuracy of 90.9% in selecting the correct expansion for given acronym on a dataset we scraped from Wikipedia with 707 distinct acronyms and 14,876 disambiguations.
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Each year, countless hours of productive research time is spent brainstorming creative acronyms for surveys, simulations, codes, and conferences. We present ACRONYM, a command-line program developed specifically to assist astronomers in identifying the best acronyms for ongoing projects. The code returns all approximately-English-language words that appear within an input string of text, regardless of whether the letters occur at the beginning of the component words (in true astronomer fashion).
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Acronyms are extensively used in web search. Typically acronym is an ambiguous one, when a user gives acronym as search query the search engine returns the results even it is not related to the user intent. To address this problem a method is proposed in this paper that, how to get the user desired web pages of the given acronym search query. The method uses two approaches they are i. Discover all multifarious definitions for the given acronym query ii.Finding popularity score and context words of each acronym definition. Acronym definitions are extracted from Google snippets, titles and acronym finder. Popularity score and context words are identified from query log files. The results evince that the proposed method performance is better than the existing system.
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Acronym Disambiguation (AD) is crucial for natural language understanding on various sources, including biomedical reports, scientific papers, and search engine queries. However, existing acronym disambiguationbenchmarks and tools are limited to specific domains, and the size of prior benchmarks is rather small. To accelerate the research on acronym disambiguation, we construct a new benchmark with three components: (1) a much larger acronym dictionary with 1.5M acronyms and 6.4M long forms; (2) a pre-training corpus with 160 million sentences;(3) three datasets that cover thegeneral, scientific, and biomedical domains.We then pre-train a language model, AcroBERT, on our constructed corpus for general acronym disambiguation, and show the challenges and values of our new benchmark.
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Abstract 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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Identification
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Acronyms are omnipresent. They usually express information that is repetitive and well known. But acronyms can also be ambiguous because there can be multiple expansions for the same acronym. In this paper, we propose a general system for acronym disambiguation that can work on any acronym given some context information. We present methods for retrieving all the possible expansions of an acronym from Wikipedia and AcronymsFinder.com. We propose to use these expansions to collect all possible contexts in which these acronyms are used and then score them using a paragraph embedding technique called Doc2Vec. This method collectively led to achieving an accuracy of 90.9% in selecting the correct expansion for given acronym, on a dataset we scraped from Wikipedia with 707 distinct acronyms and 14,876 disambiguations.
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This paper proposes an automatic method for disambiguating an acronym with multiple definitions, considering the context surrounding the acronym. First, the method obtains the Web pages that include both the acronym and its definitions. Second, the method feeds them to the machine learner. Cross-validation tests results indicate that the current accuracy of obtaining the appropriate definition for an acronym is around 92% for two ambiguous definitions and around 86% for five ambiguous definitions.
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