Named Entity Recognition from Gujarati Text Using Rule-Based Approach

Shah, Dikshan N.; Bhadka, Harshad

doi:10.1007/978-3-319-76348-4_76

Cited by 4 publications

(4 citation statements)

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“…The approach was evaluated, and the findings indicated that evidence-based dietary recommendations (drNER) produce good results and may be used to extract knowledge about evidence-based dietary guidelines. Shah and Bhadka [37] demonstrated a Rule-based technique to extract various named items from a text document. Three separate test cases have been calculated on the training dataset using stated criteria with an accuracy of 70%.…”

Section: Deep Learning Approachmentioning

confidence: 99%

“…(1) Ambiguity: Words can have multiple meanings or belong to different categories based on context [37]. For example: भारत ने मै च जीता.…”

Section: Introductionmentioning

confidence: 99%

“…(3) Lack of Content: Sometimes, the necessary content to identify an entity is not present [37]. (4) Data Annotation: Manually annotating data for NER is time-consuming and the quality of the labeled data can impact the performance a of model.…”

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: Deep Learning Approachmentioning

confidence: 99%

“…(1) Ambiguity: Words can have multiple meanings or belong to different categories based on context [37]. For example: भारत ने मै च जीता.…”

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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“…Also, these systems cannot be translated into other languages. This approach was used for years before the emergence of machine learning era; as with the rise of machine learning the first step was the adoption of both approaches into hybrid solution [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning for Arabic Named Entity Recognition With Deep Neural Networks

et al. 2020

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The vast amount of unstructured data spread on a daily basis rises the need for developing effective information retrieval and extraction methods. Named Entity Recognition is a challenging classification task for structuring data into pre-defined labels, and is even more complicated when being applied on the Arabic language due to its special traits and complex nature. This article presents a novel Deep Learning approach for Standard Arabic Named Entity Recognition that proved its out-performance when being compared to previous works. The main aim of building a new model is to provide better fine-grained results for use in the Natural Language Processing fields. In our proposed methodology we utilized transfer learning with deep neural networks to build a Pooled-GRU model combined with the Multilingual Universal Sentence Encoder. Our proposed model scored about 17% enhancement when being compared to previous work. INDEX TERMS Natural language processing, deep learning, transfer learning, ANER, universal sentence encoder, Bi-LSTM.

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