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 different morphologies and thus involve dissimilar NER procedures. For example, an Arabic NER system cannot be practically used in processing Malay texts due to the different morphological features. The morphological features of every language are rich and complex and donates to the difficulties of implementing an actual method to develop the accurate NER system. In this paper, we review on three main techniques that commonly used to develop an NER system well-known as Rule-Based, Machine Learning, and Hybrid approach. This paper also highlights the features of each technique.

State-of-art approach for Indian Language based on NER: Comprehensive Review

“…(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].…”

“…(b) Question Answering Systems: Named Entity Recognition helps in identifying entities mentioned in a question and linking them to relevant information in a knowledge base. This enables question answering systems to provide more accurate and relevant answers [4], [12], [39], [41], [42], [44], [45]. (e) Social Media Analysis: NER is valuable for analyzing social media data.…”

“…(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].…”

“…(g) Machine Translation (MT): NER can assist in improving machine translation systems by correctly identifying and preserving named entities during the translation process [39]. This ensures that names of people, organizations and locations are accurately translated [4], [41], [47], [48].…”