A Study on Named Entity Recognition with Different Word Embeddings on GMB Dataset using Deep Learning Pipelines

Chavali, Surya Teja; Kandavalli, Charan Tej; Sugash, T M; Gupta, Deepa

doi:10.1109/icccnt54827.2022.9984220

Cited by 3 publications

(2 citation statements)

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“…The proposed approach was found to outperform the baselines in terms of both NER accuracy and overall question answering accuracy. In addition, Chavali et al [22] compared the performance of different word embeddings on the GMB dataset for named entity recognition using deep learning pipelines. The authors compared several word embeddings and found that GloVe embeddings outperformed the others in terms of both F1 scores and 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%