Results 21 to 30 of about 9,795 (189)
State-of-the-art Chinese Word Segmentation with Bi-LSTMs [PDF]
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 2018 A wide variety of neural-network architectures have been proposed for the task of Chinese word segmentation. Surprisingly, we find that a bidirectional LSTM model, when combined with standard deep learning techniques and best practices, can achieve better accuracy on many of the popular datasets as compared to models based on more complex neural ...
Ji Ma 0004 +2 more
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A Proposed Bi-LSTM Method to Fake News Detection
2022 International Conference for Advancement in Technology (ICONAT), 2022 Recent years have seen an explosion in social media usage, allowing people to connect with others. Since the appearance of platforms such as Facebook and Twitter, such platforms influence how we speak, think, and behave. This problem negatively undermines confidence in content because of the existence of fake news.
Taminul Islam +5 more
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Character gated recurrent neural networks for Arabic sentiment analysis
Scientific Reports, 2022 Sentiment analysis is a Natural Language Processing (NLP) task concerned with opinions, attitudes, emotions, and feelings. It applies NLP techniques for identifying and detecting personal information from opinionated text.
Eslam Omara, Mervat Mousa, Nabil Ismail
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Bi-LSTM Price Prediction based on Attention Mechanism
CoRR, 2022 With the increasing enrichment and development of the financial derivatives market, the frequency of transactions is also faster and faster. Due to human limitations, algorithms and automatic trading have recently become the focus of discussion. In this paper, we propose a bidirectional LSTM neural network based on an attention mechanism, which is ...
Jiashu Lou, Leyi Cui, Ye Li
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Hierarchical Bi-LSTM based emotion analysis of textual data [PDF]
Bulletin of the Polish Academy of Sciences: Technical Sciences, 2022 Nowadays, Twitter is one of the most popular microblogging sites that is generating a massive amount of textual data. Such textual data is intended to incorporate human feelings and opinions with related events like tweets, posts, and status updates.
Dashrath Mahto, Subhash Chandra Yadav
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IEEE Access, 2021 Weaponry equipment names belong to an important military naming entity that is difficult to identify because of features, such as complex components, miscellaneous, and scarce annotation corpus. Here, the automatic recognition of weaponry equipment names
Chenguang Liu +3 more
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Long Short-Term Memory and Bidirectional Long Short-Term Memory Algorithms for Sentiment Analysis of Skintific Product Reviews [PDF]
ITM Web of ConferencesIn the era of ever-evolving digital technology, conducting customer sentiment analysis through product reviews has become crucial for businesses to improve their offerings and increase customer satisfaction. This research aims to analyze the sentiment of
Simanihuruk Laurensia, Suparwito Hari
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Prediction of Multi-Site PM2.5 Concentrations in Beijing Using CNN-Bi LSTM with CBAM
Atmosphere, 2022 Air pollution is a growing problem and poses a challenge to people’s healthy lives. Accurate prediction of air pollutant concentrations is considered the key to air pollution warning and management.
Dong Li, Jiping Liu, Yangyang Zhao
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Captioning of Image Conceptually Using BI-LSTM Technique
, 2021 Due to the fact of increase in amount of video data each day, the need for auto generation of captioning them clearly is inevitable. Video captioning makes the video more accessible in numerous ways. It allows the deaf and hard of hearing individuals to watch videos, helps people to focus on and remember the information more easily, and lets people ...
Thaseena Sulthana +4 more
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A Bidirectional Deep-Learning Algorithm to Forecast Regional Ionospheric TEC Maps
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 The rapid evolutions in artificial intelligence and the machine learning era have significantly improved accuracy for ionospheric space weather forecasting models.
Kondaveeti Sivakrishna +2 more
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