Named Entity Recognition Using Conditional Random Fields

Khan, Wahab; Daud, Ali; Shahzad, Khurram; Amjad, Tehmina; Banjar, Ameen; Fasihuddin, Heba

doi:10.3390/app12136391

Cited by 13 publications

(6 citation statements)

References 27 publications

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“…They achieved a better accuracy of 72.27% for NE classification and performance of 62.64% for NE identification using Machine Learning approach. Khan et al [72] conducted a method based on Conditional-Random-Field along with the features of independent language and dependent language like context windows of words and POS tags. The authors also contributed the maximum amount of Named Entity types for dataset i.e., UrduNER (UNER-I) which is manually annotated and the usability and effectiveness of the recommended method were evaluated through experiments using both the dataset we created and an existing dataset.…”

Section: (B) Machine Learning Approachmentioning

confidence: 99%

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: (B) Machine Learning Approachmentioning

confidence: 99%

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

Pandey,

Nathani

2024

Preprint

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“…However, this study lacks error analysis, results validation, and comparison with existing state-of-the-art studies. Wahab et al [ 18 ] apply a supervised CRF approach to the IJCNLP dataset for seven NE types. They use the existing CRF-based approach as a baseline and use the same features on the newly developed UNER dataset [ 44 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Machine Learning (ML) based studies use supervised learning techniques that can easily be deployed across domains and hence require comparatively less development effort [17]. Various studies have used models including Conditional Random Fields (CRF) [18], the Hidden Markov Model (HMM) [19], and the Maximum Entropy Model (MEM) [20] etc. These studies outperform the rule-based approach but the success of these models heavily depends on the selection of the features that are derived from the annotated corpus and are used in the training process.…”

Section: Introductionmentioning

confidence: 99%

A deep learning approach for Named Entity Recognition in Urdu language

Anam,

Anwar,

Jamal

et al. 2024

PLoS ONE

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Named Entity Recognition (NER) is a natural language processing task that has been widely explored for different languages in the recent decade but is still an under-researched area for the Urdu language due to its rich morphology and language complexities. Existing state-of-the-art studies on Urdu NER use various deep-learning approaches through automatic feature selection using word embeddings. This paper presents a deep learning approach for Urdu NER that harnesses FastText and Floret word embeddings to capture the contextual information of words by considering the surrounding context of words for improved feature extraction. The pre-trained FastText and Floret word embeddings are publicly available for Urdu language which are utilized to generate feature vectors of four benchmark Urdu language datasets. These features are then used as input to train various combinations of Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), CRF, and deep learning models. The results show that our proposed approach significantly outperforms existing state-of-the-art studies on Urdu NER, achieving an F-score of up to 0.98 when using BiLSTM+GRU with Floret embeddings. Error analysis shows a low classification error rate ranging from 1.24% to 3.63% across various datasets showing the robustness of the proposed approach. The performance comparison shows that the proposed approach significantly outperforms similar existing studies.

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“…[6] introduced an interesting study which built a "Thesaurus-based Named Entity Recognition System for detecting spatio-temporal crime events in Spanish language from Twitter" system, which is specific to the Spanish language. In exploring the various studies conducted on NER applications, we observe that the solutions are either language-specific ( [7], [8], [9]) or problemspecific ( [10], [3], [11]).…”

Section: Non-chatgpt Approaches To Named Entity Recognitionmentioning

confidence: 99%

Untitled

2023

IJNLC

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Terrorism has become a worldwide plague with severe consequences for the development of nations. Besides killing innocent people daily and preventing educational activities from taking place, terrorism is also hindering economic growth. Machine Learning (ML) and Natural Language Processing (NLP) can contribute to fighting terrorism by predicting in real-time future terrorist attacks if accurate data is available. This paper is part of a research project that uses text from social networks to extract necessary information to build an adequate dataset for terrorist attack prediction. We collected a set of 3000 social network texts about terrorism in Burkina Faso and used a subset to experiment with existing NLP solutions. The experiment reveals that existing solutions have poor accuracy for location recognition, which our solution resolves. We will extend the solution to extract dates and action information to achieve the project's goal.

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Named Entity Recognition Using Conditional Random Fields

Cited by 13 publications

References 27 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

A deep learning approach for Named Entity Recognition in Urdu language

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