Improving the BERT Model with Proposed Named Entity Recognition Method for Question Answering

Güven, Zekeriya Anıl; Ünalır, Murat Osman

doi:10.1109/ubmk52708.2021.9558992

Cited by 4 publications

(5 citation statements)

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“…Guven and Unalir [21] used a dataset of questions and answers and compared their approach with several baseline methods. The proposed approach was found to outperform the baselines in terms of both NER accuracy and overall question answering accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…Proposed approach improves factual accuracy of abstractive text summarization [21] Improving the BERT model with proposed named entity recognition method for question answering BERT, NER Proposed approach improves performance of BERT for question answering [22] A study on named entity recognition with different word embeddings on GMB dataset using deep learning pipelines Deep learning, word embeddings, NER Provides comparison of different word embeddings for NER on the GMB dataset…”

Section: Abstractive Text Summarization Multi-objective Optimizationmentioning

confidence: 99%

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Text summarization of News Articles based on named entity recognition using Spacy library

Alshibly

Al-Shorfat

Otair

et al. 2023

Preprint

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The study discusses the importance of summarization in dealing with a large amount of data available on the internet. The study used a deep-learning algorithm based on functions from the spacy library in Python to summarize news articles and evaluated the impact of named entity recognition on the summarization process. The study assessed different datasets from CNN-DailyMail and the BBC (entertainment articles) and found that the proposed method based on named entity recognition showed significant improvement in recall, precision, and F-score compared to the word frequency method. The study also observed that the articles from CNN-DailyMail were longer, with an average of 551 words and 28 sentences, compared to the BBC (entertainment articles), which had an average of 190 words and 12 sentences. The evaluation results showed that the proposed method based on named entity recognition performed better on the shorter articles from the BBC, indicating that the method was more effective in summarizing shorter texts. In summary, the study highlighted the importance of summarization in dealing with a large amount of data available on the internet. It showed that named entity recognition can significantly improve the effectiveness of the summarization process. The study also observed that the proposed method was more effective in summarizing shorter texts.

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Section: Related Workmentioning

confidence: 99%

Section: Abstractive Text Summarization Multi-objective Optimizationmentioning

confidence: 99%

Text summarization of News Articles based on named entity recognition using Spacy library

Alshibly

Al-Shorfat

Otair

et al. 2023

Preprint

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“…Это дает богатый спектр исследовательских задач для национальных языков. тематическая классификация английский weighting scheme NE авторский - [83] тематическая классификация английский BiLSTM + CRF авторский 88.0 [84] машинный перевод английский flairNLP субтитры немецкий [85] анализ тональности английский Stanford Named Entity Tagger SES, MSR2016 - [86] анализ тональности английский LSTM авторский - [63] онтология английский BERT-BiLSTM-CRF авторский 81.31 китайский [87] вопросно-ответные системы английский BERT авторский - [88] определение лжи английский spaCy, Stanford's NER отзывы -С одной стороны, NER является классической самостоятельной задачей NLP. С другой стороны, есть ряд задач в области обработки текста и извлечения информации, в которые NER входит как подзадача или часть технологии решения.…”

Section: Ner в предметных областяхunclassified

“…Разработанный AliMe KG, граф знаний в электронной коммерции, помогает понять потребности пользователей, ответить на их вопросы и создать поясняющие тексты, в том числе руководство по покупкам, ответы на вопросы о недвижимости, создание точек продаж. Другое исследование [87] напрямую изучает вопрос NER в вопросно-ответных системах.…”

Section: Ner в задачах Nlpunclassified

Name Entity Recognition Tasks: Technologies and Tools

Lagutina

Vasilyev

Zafievsky

2023

Model. anal. inf. sist.

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The task of named entity recognition (NER) is to identify and classify words and phrases denoting named entities, such as people, organizations, geographical names, dates, events, terms from subject areas. While searching for the best solution, researchers conduct a wide range of experiments with different technologies and input data. Comparison of the results of these experiments shows a significant discrepancy in the quality of NER and poses the problem of determining the conditions and limitations for the application of the used technologies, as well as finding new solutions. An important part in answering these questions is the systematization and analysis of current research and the publication of relevant reviews. In the field of named entity recognition, the authors of analytical articles primarily consider mathematical methods of identification and classification and do not pay attention to the specifics of the problem itself. In this survey, the field of named entity recognition is considered from the point of view of individual task categories. The authors identified five categories: the classical task of NER, NER subtasks, NER in social media, NER in domain, NER in natural language processing (NLP) tasks. For each category the authors discuss the quality of the solution, features of the methods, problems, and limitations. Information about current scientific works of each category is given in the form of a table for clarity. The review allows us to draw a number of conclusions. Deep learning methods are leading among state-of-the-art technologies. The main problems are the lack of datasets in open access, high requirements for computing resources, the lack of error analysis. A promising area of research in NER is the development of methods based on unsupervised techniques or rule-base learning. Intensively developing language models in existing NLP tools can serve as a possible basis for text preprocessing for NER methods. The article ends with a description and results of experiments with NER tools for Russian-language texts.

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“…Although named entity recognition corpora exist, they are primarily focused on Wikipedia (a structured text) [6]. However, since carbonate platform literature is an informal text with fewer restrictions on the style of writing papers, entity extraction in the discipline requires its corpus.…”

Section: Introductionmentioning

confidence: 99%

Corpus of Carbonate Platforms with Lexical Annotations for Named Entity Recognition

Hu¹,

Ren²,

Jiang³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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An obviously challenging problem in named entity recognition is the construction of the kind data set of entities. Although some research has been conducted on entity database construction, the majority of them are directed at Wikipedia or the minority at structured entities such as people, locations and organizational nouns in the news. This paper focuses on the identification of scientific entities in carbonate platforms in English literature, using the example of carbonate platforms in sedimentology. Firstly, based on the fact that the reasons for writing literature in key disciplines are likely to be provided by multidisciplinary experts, this paper designs a literature content extraction method that allows dealing with complex text structures. Secondly, based on the literature extraction content, we formalize the entity extraction task (lexicon and lexical-based entity extraction) for entity extraction. Furthermore, for testing the accuracy of entity extraction, three currently popular recognition methods are chosen to perform entity detection in this paper. Experiments show that the entity data set provided by the lexicon and lexical-based entity extraction method is of significant assistance for the named entity recognition task. This study presents a pilot study of entity extraction, which involves the use of a complex structure and specialized literature on carbonate platforms in English.

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Improving the BERT Model with Proposed Named Entity Recognition Method for Question Answering

Cited by 4 publications

References 0 publications

Text summarization of News Articles based on named entity recognition using Spacy library

Text summarization of News Articles based on named entity recognition using Spacy library

Name Entity Recognition Tasks: Technologies and Tools

Corpus of Carbonate Platforms with Lexical Annotations for Named Entity Recognition

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