A survey on Named Entity Recognition — datasets, tools, and methodologies

Jehangir, Basra; Radhakrishnan, Saravanan; Agarwal, Rahul

doi:10.1016/j.nlp.2023.100017

Cited by 34 publications

(9 citation statements)

References 61 publications

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“…Text mining is applied in social media through different techniques, such as sentiment analysis, topic modeling, named entity recognition (NER), user profiling and behavior analysis, anomaly and event detection, and social network analysis (SNA) [46][47][48][49][50][51].…”

Section: Literature Reviewmentioning

confidence: 99%

Assessing the EU Climate and Energy Policy Priorities for Transport and Mobility through the Analysis of User-Generated Social Media Content Based on Text-Mining Techniques

Nikolaidou,

Kopsacheilis,

Gavanas

et al. 2024

Sustainability

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For over three decades, the European Union’s (EU) transport policy has aimed at fostering environmental sustainability and energy efficiency. Since 2015, European policymakers have focused more on three key sustainable development goals: decarbonizing the transport system, promoting low-emission mobility solutions, and transitioning to renewable and alternative fuels. To effectively communicate priorities and engage stakeholders, EU policymakers regularly use social media platforms like Twitter (now known as X). This active discourse involves policymakers, industrial stakeholders, the media, and the public, offering insights into the role of transport policy in addressing climate change and energy transition challenges. The current research endeavors to track and analyze the evolution of user-generated content related to climate change, energy transition, and smart mobility on Twitter from 2011 to 2021. This research uses text-mining and social network analysis techniques to quantitatively and qualitatively assess the dynamics of relevant EU policies and their effects. The study’s findings can be used to establish a robust monitoring and evaluation framework at the EU and national levels. This framework will assess the effectiveness of communicating strategic priorities for sustainable transport development. It also holds potential for application in other sectors, broadening its impact.

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Section: Literature Reviewmentioning

confidence: 99%

Assessing the EU Climate and Energy Policy Priorities for Transport and Mobility through the Analysis of User-Generated Social Media Content Based on Text-Mining Techniques

Nikolaidou,

Kopsacheilis,

Gavanas

et al. 2024

Sustainability

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“…In fact, domain-specific re-pre-training aims to resolve even more complex scientific ambiguities, such as complex noun phrases, containing prepositions, determiners, adjectives, and nouns, frequently labelled as entities in their entirety [17], such as SciERC terms 'space of candidate regions' and 'regular expressions' [18]. Such noun phrases are frequently present in scientific language [17], with the potential for entities nested inside these noun phrases [10]. This highlights a potential challenge in entity boundary definition for scientific Named Entity Recognition.…”

Section: Transformer Pre-trainingmentioning

confidence: 99%

“…Current research fails to evaluate potential performance constraints related to the singular scientific dataset employed in all research to date, Scientific Entities, Relations and Coreferences (SciERC). SemEval 2017 and 2018 [17,18] are noted as two alternatives. However, the former is proven to have a bias towards NER, and the latter only annotates intra-sentence relations [18], which is unrepresentative of natural language.…”

Section: Research Gapmentioning

confidence: 99%

“…Current research fails to evaluate potential performance constraints related to singular scientific dataset employed in all research to date, Scientific Entities, Rela and Coreferences (SciERC). SemEval 2017 and 2018 [17,18] are noted as two alternat However, the former is proven to have a bias towards NER, and the latter only anno intra-sentence relations [18], which is unrepresentative of natural language. Current research fails to evaluate potential performance constraints related to the singular scientific dataset employed in all research to date, Scientific Entities, Relations and Coreferences (SciERC).…”

Section: Research Gapmentioning

confidence: 99%

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Leveraging Semantic Text Analysis to Improve the Performance of Transformer-Based Relation Extraction

Evans,

Latifi,

Ahsan

et al. 2024

Information

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Keyword extraction from Knowledge Bases underpins the definition of relevancy in Digital Library search systems. However, it is the pertinent task of Joint Relation Extraction, which populates the Knowledge Bases from which results are retrieved. Recent work focuses on fine-tuned, Pre-trained Transformers. Yet, F1 scores for scientific literature achieve just 53.2, versus 69 in the general domain. The research demonstrates the failure of existing work to evidence the rationale for optimisations to finetuned classifiers. In contrast, emerging research subjectively adopts the common belief that Natural Language Processing techniques fail to derive context and shared knowledge. In fact, global context and shared knowledge account for just 10.4% and 11.2% of total relation misclassifications, respectively. In this work, the novel employment of semantic text analysis presents objective challenges for the Transformer-based classification of Joint Relation Extraction. This is the first known work to quantify that pipelined error propagation accounts for 45.3% of total relation misclassifications, the most poignant challenge in this domain. More specifically, Part-of-Speech tagging highlights the misclassification of complex noun phrases, accounting for 25.47% of relation misclassifications. Furthermore, this study identifies two limitations in the purported bidirectionality of the Bidirectional Encoder Representations from Transformers (BERT) Pre-trained Language Model. Firstly, there is a notable imbalance in the misclassification of right-to-left relations, which occurs at a rate double that of left-to-right relations. Additionally, a failure to recognise local context through determiners and prepositions contributes to 16.04% of misclassifications. Furthermore, it is highlighted that the annotation scheme of the singular dataset utilised in existing research, Scientific Entities, Relations and Coreferences (SciERC), is marred by ambiguity. Notably, two asymmetric relations within this dataset achieve recall rates of only 10% and 29%.

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“…As for the used techniques and methods applied to NER, there are mainly four approaches according to Jehangir et al (2023): rule-based algorithms, supervised and unsupervised machine learning algorithms, and deep-learning algorithms. The rule based approach relies on predefined grammatical rules and dictionaries to identify named entities.…”

Section: Introductionmentioning

confidence: 99%

ELYADATA at WojoodNER Shared Task: Data and Model-centric Approaches for Arabic Flat and Nested NER

Laouirine,

Elleuch,

Bougares

2023

Proceedings of ArabicNLP 2023

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This paper describes our submissions to the WojoodNER shared task organized during the first ArabicNLP conference. We participated in the two proposed sub-tasks of flat and nested Named Entity Recognition (NER). Our systems were ranked first over eight and third over eleven in the Nested NER and Flat NER, respectively. All our primary submissions are based on DiffusionNER models, where the NER task is formulated as a boundary-denoising diffusion process. Experiments on nested Wojood-NER achieves the best results with a micro F1-score of 93.73%. For the flat sub-task, our primary system was the third-best system, with a micro F1-score of 91.92% 1 .

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A survey on Named Entity Recognition — datasets, tools, and methodologies

Cited by 34 publications

References 61 publications

Assessing the EU Climate and Energy Policy Priorities for Transport and Mobility through the Analysis of User-Generated Social Media Content Based on Text-Mining Techniques

Assessing the EU Climate and Energy Policy Priorities for Transport and Mobility through the Analysis of User-Generated Social Media Content Based on Text-Mining Techniques

Leveraging Semantic Text Analysis to Improve the Performance of Transformer-Based Relation Extraction

ELYADATA at WojoodNER Shared Task: Data and Model-centric Approaches for Arabic Flat and Nested NER

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