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AI-Driven Hybrid Detection and Classification Framework for Secure Sleep Health IoT Networks. [PDF]
Clocks SleepValsalan P, Siddiqui MM.
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Tatt‐BiLSTM: Web service classification with topical attention‐based BiLSTM
Concurrency and Computation: Practice and Experience, 2021AbstractWith the rapid growth of the number of Web services on the Internet, how to classify Web services correctly and efficiently become particularly important in service management tasks, such as service discovery, service selection, service ranking, and service recommendation. Existing functionality‐based service classification techniques have some
Guosheng Kang +6 more
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n-BiLSTM: BiLSTM with n-gram Features for Text Classification
2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC), 2020Text classification is widely existing in the fields of e-commerce and log message analysis. Besides, it is an essential module in text processing tasks. In this paper, we present a method to create an accurate and fast text classification system in both One-vs.-one and One-vs.-rest manner.
Yunxiang Zhang, Zhuyi Rao
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BiLSTM Embedding Pretraining for Relation Extraction
2021 International Joint Conference on Neural Networks (IJCNN), 2021Many recent relation extraction methods are proposed based on distantly supervised learning to address the issue of expensive human labelling. They automatically align relation instances from a knowledge base with unstructured text. Although these methods solve the problem of insufficient labelled data and are scalable, due to automatic labelling, they
Haojie Huang 0004, Raymond K. Wong 0001
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BiLSTM-Autoencoder Architecture for Stance Prediction
2020 International Conference on Data Science and Engineering (ICDSE), 2020The recent surge in the abundance of fake news appearing on social media and news websites poses a potential threat to high-quality journalism. Misinformation hurts people, society, science, and democracy. This reason has led many researchers to develop techniques to identify fake news.
S. Meena Padnekar +2 more
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Coattention based BiLSTM for answer selection
2017 IEEE International Conference on Information and Automation (ICIA), 2017Attention based recurrent neural networks have achieved great success in answer selection, which is an important subtask of question answering (QA). However, previous work used fixed representation of question to compute the attention information for answers, which fails to extract the influence that answers make on question.
Lei Zhang, Longxuan Ma
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Dataset: BiLSTM-VHP: BiLSTM-based Network for Viral Host Prediction
2023Dataset and all the related files for BiLSTM-VHP: BiLSTM-based Network for Viral Host ...
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Speaker-Dependent BiLSTM-Based Phrasing
2020Phrase boundary detection is an important part of text-to-speech systems since it ensures more natural speech synthesis outputs. However, the problem of phrasing is ambiguous, especially per speaker and per style. This is the reason why this paper focuses on speaker-dependent phrasing for the purposes of speech synthesis, using a neural network model ...
Markéta Juzová, Daniel Tihelka
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AABC:ALBERT-BiLSTM-CRF Combining with Adapters
2021Pre-training models (PTMs) are language models pre-trained on a large corpus, which can learn general language representations through training tasks within the model. PTMs complete various NLP tasks by connecting with downstream models. PTMs can avoid building new models from the beginning. Therefore, they are widely used in the NLP field. In order to
Jiayan Wang +3 more
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