2019
DOI: 10.1088/1757-899x/551/1/012052
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A Concise Review of Named Entity Recognition System: Methods and Features

Abstract: Named Entity Recognition (NER) is an elementary tool for all application areas in Natural Language Processing (NLP) such as Automatic Summarization, Information Extraction, Information Retrieval, Text Mining, Machine Translation, Question Answering, and Genetics. NER is a task to discover and categorises the named entities (‘atomic elements’) in the text into predefined classes such as the names of persons, organizations, locations, terminologies of time, quantity and etc. Different languages may have differen… Show more

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Cited by 4 publications
(4 citation statements)
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“…(f) Information Retrieval (IR): NER aids in improving search results by identifying and extracting entities from search queries or documents [39]. It helps search engines understand the context and intent behind the query, leading to more accurate and relevant results [3], [41], [42], [43], [46].…”
Section: Application Of Nermentioning
confidence: 99%
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“…(f) Information Retrieval (IR): NER aids in improving search results by identifying and extracting entities from search queries or documents [39]. It helps search engines understand the context and intent behind the query, leading to more accurate and relevant results [3], [41], [42], [43], [46].…”
Section: Application Of Nermentioning
confidence: 99%
“…(a) Information Extraction (IE): Named Entity Recognition is widely used in information extraction systems to identify and extract specific pieces of information from unstructured text [38], [39]. For example, extracting names of people, organizations, and locations from news articles or social media posts [40], [41], [42], [43], [44].…”
Section: Application Of Nermentioning
confidence: 99%
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