A Survey on Various Approach used in Named Entity Recognition for Indian Languages

Shah, Dikshan N.; Bhadka, Harshad

doi:10.5120/ijca2017913878

Cited by 7 publications

(9 citation statements)

References 6 publications

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“…(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.…”

Section: Application Of Nermentioning

confidence: 99%

“…Language is the foundation of NLP, as it focuses on enabling computers to understand, interpret and generate human language [3]. NLP helps in bridging the gap between human communication and machine understanding [4], [5]. NLP algorithms and models are designed to handle the complexities of language [1], [4], [6], [7], [8], [9].…”

Section: Introductionmentioning

confidence: 99%

“…Based on Information retrieval systems require the contextual data that surrounds NEs where names perform an important role in information retrieval as significant information is always surrounded by NEs [27], [28]. Names have traditionally subscribed to the belief that automatic text summarization [29], biomedical data and text summarization systems can effectively use investigated NEs as they remain intact in all languages [4], [30], [31] NER models are typically trained using supervised machine learning techniques, where annotated training data is used to teach the model to recognize and classify named entities. These models employ various linguistic features, such as part-of-speech tags, word context, and syntactic information, to make predictions about the entities present in the text.…”

Section: Introductionmentioning

confidence: 99%

“…NLP helps in bridging the gap between human communication and machine understanding [4], [5]. NLP algorithms and models are designed to handle the complexities of language [1], [4], [6], [7], [8], [9]. NER stands for Name Entity Recognition, is a task of NLP that involves identifying and classifying named entities, like names of person, location, organization and data [3], [5], [10], [11], [12], [13], [14], [15].…”

Section: Introductionmentioning

confidence: 99%

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

Pandey,

Nathani

2024

Preprint

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Named Entity Recognition (NER) is a fundamental task of natural language processing (NLP) that focuses on the identification and classification of named entities such as name of individual persons, location, organization and dates within the text. NER plays a pivotal role in various NLP applications, including information extraction, question answering, text summarization and sentiment analysis. Natural language processing's (NLPs) fundamental issue is named entity recognition (NER). While extensive research has been conducted on NER for English and Hindi, the complexities of Indian languages present unique challenges that require customized solutions. Working with NER for Indian languages is a difficult endeavor with limited resources available. This article provides a comprehensive review of NER approaches tailored for Indian languages. Indian languages pose unique challenges to NER due to their rich morphological and syntactic variations, script diversity and limited annotated data availability. This paper reviews the various techniques and methodologies employed in NER for Indian languages, including rule-based, machine learning and deep learning approaches. It analyzes the strengths and limitations of each approach. Additionally, this article examines the recent advancements in transfer learning and multilingual models, showcasing their potential in improving NER performance across Indian languages. This paper aims to guide researchers and practitioners in the development of NER systems for Indian languages and foster further advancements in this field. This article also provides a comprehensive review of the diverse approaches employed for NER in Indian languages, highlighting the strength and limitations as well.

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Section: Application Of Nermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Pandey,

Nathani

2024

Preprint

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“…In this section, we discuss NER and custom NER (user-defined NER). NER, as a sub-task of NLP, refers to the process of data extraction to recognize and classify named entities in unstructured text data into their appropriate categories including locations, organizations, times, etc., Shah and Bhadka (2017); Stepanyan (2020). The applicability of NER models is broad, ranging from recognizing dates and cities in chatbots to open-domain question answering Tarcar et al (2019).…”

Section: Related Workmentioning

confidence: 99%

Customized Named Entity Recognition Using Bert for the Social Learning Management System Platform CourseNetworking

Padmanandam,

Sunitha,

Jafari

et al. 2024

Journal of Computer Science

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Named Entity Recognition (NER) is an information extraction task and one of the most researched applications to extract knowledge from massive data. Conventional NER systems identify predefined entities like name, person, location, organization, time, etc. However, there is a limitation to identifying user-defined entities that are specific to an application. This challenge introduces the concept of customized NER. For instance, if a learning management system like CourseNetworking (CN) needs to identify the skill set of a user from their posts, the existing pre-trained NER models cannot be used. To overcome this information extraction limitation, we propose a customized named entity recognition system for the CN platform using the deep learning model, Bi-Directional Encoder Representation from Transformer (BERT) which is a transformer-based deep learning technique where all output elements are connected to all input elements with dynamic weight connections. The proposed customization model can be employed to train any entity of user choice with a decent amount of training dataset. The model shows 70-72% recall and F1-Score varied on the number of epochs trained. This model is used in various applications like fraudulent detection, recommendation systems, and business intelligence.

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Gujarati Task Oriented Dialogue Slot Tagging Using Deep Neural Network Models

Parikh

Joshi

2021

Soft Computing and Its Engineering Applications

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A Survey on Various Approach used in Named Entity Recognition for Indian Languages

Cited by 7 publications

References 6 publications

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

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

Customized Named Entity Recognition Using Bert for the Social Learning Management System Platform CourseNetworking

Gujarati Task Oriented Dialogue Slot Tagging Using Deep Neural Network Models

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