Named entity recognition in Odia language: a rule-based approach

Anandika, Amrita; Chakravarty, Sujata; Paikaray, Bijay

doi:10.1504/ijris.2023.128379

Cited by 6 publications

(5 citation statements)

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“…However, the formal grammar of the natural language is highly complex, difficult to summarize, and large in number. The rule-based method is highly complex, has high requirements for the correctness of rules, and the accuracy is easy to degrade ( Anandika, Sujata & BijayKumar, 2023 ; Andrade Sequoia & Hannah, 2023 ).…”

Section: Related Workmentioning

confidence: 99%

Fuzzy information recognition and translation processing in English interpretation based on a generalized maximum likelihood ratio algorithm

Yin

2024

PeerJ Computer Science

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English interpretation plays a vital role as a critical link in cross-language communication. However, there are various types of ambiguous information in many interpreting scenarios, such as ambiguity, ambiguous vocabulary, and syntactic structures, which may lead to inaccuracies and fluency issues in translation. This article proposes a method based on the generalized maximum likelihood ratio algorithm (GLR) to identify and process fuzzy information in English interpretation to improve the quality and efficiency of performance. Firstly, we systematically analyzed the common types of fuzzy information in interpretation and delved into the basic principles and applications of the generalized maximum likelihood ratio algorithm. This algorithm is widely used in natural language processing to solve uncertainty problems and has robust modeling and inference capabilities, making it suitable for handling fuzzy information in interpretation. Then, we propose a fuzzy information recognition model based on the generalized maximum likelihood ratio algorithm. This model utilizes a large-scale interpretation corpus for training and identifies potential fuzzy information in the interpretation process through statistical analysis and pattern recognition. Once fuzzy information is detected, we adopt a series of effective translation processing strategies, including contextual inference and adaptation, to ensure the accuracy and naturalness of interpretation. Finally, we conducted a series of experiments to evaluate the performance of the proposed method. The experimental results show that the fuzzy information recognition and translation processing method based on the generalized maximum likelihood ratio algorithm performs well in different interpretation scenarios, significantly improving the quality and fluency of interpretation and reducing ambiguity caused by fuzzy information.

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Section: Related Workmentioning

confidence: 99%

Fuzzy information recognition and translation processing in English interpretation based on a generalized maximum likelihood ratio algorithm

Yin

2024

PeerJ Computer Science

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“…Anandika et at. [1] demonstrated a work for traditional technique using Rule-based approach in order to build a system for the automatic detection of Named Entities(NEs) from tourist domain dataset. The accuracy acheived of 83% and 71% respectively which has been obtained by the algorithm while considering words with and without its repetition.Jumani et al [16] investigated the issue of NER in the Sindhi Language and proposed a solution.…”

Section: Taxonomy Of Ner Approaches For Indian Languagesmentioning

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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“…A syntactic function based on analytic linear table is obtained by correcting the structural ambiguity in Chinese and English. The main contribution of this paper is (1) The method designed in this paper can automatically search for phrases.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Information Recognition and Translation Processing in English Interpretation based on Artificial Intelligence Recognition Technology

Li¹

2023

Preprint

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Interpretation is an oral expression that converts the information heard and understood in the source language into the target language quickly and accurately, thus completing the role of information transmission. However, language contains too much fuzzy information, so it is inevitable to have fuzzy information in interpretation. The characteristics of fuzzy information, the differences between different languages and cultural backgrounds, and the unpredictability of interpretation have brought great challenges to interpretation. This paper proposes an improved generalized maximum likelihood ratio algorithm (GLR) for fuzzy information processing in English. To improve interpretation accuracy, this study analyzes the characteristics of language databases, vocabulary, grammar, and translation. More specifically, the principle of natural language processing research via intelligent recognition technology is introduced in this study. Secondly, the author introduces the role of vague language in oral communication. Then, this paper introduces the fuzzy language processing method via the improved GLR method in detail. Finally, the experimental results are given to verify the effectiveness of the method.

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Named entity recognition in Odia language: a rule-based approach

Cited by 6 publications

References 0 publications

Fuzzy information recognition and translation processing in English interpretation based on a generalized maximum likelihood ratio algorithm

Fuzzy information recognition and translation processing in English interpretation based on a generalized maximum likelihood ratio algorithm

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

Fuzzy Information Recognition and Translation Processing in English Interpretation based on Artificial Intelligence Recognition Technology

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