Author
Ying Yang
Bio: Ying Yang is an academic researcher from Nanjing University of Information Science and Technology. The author has contributed to research in topics: Graph & Mutual information. The author has co-authored 1 publications.
Papers
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TL;DR: Zhang et al. as discussed by the authors applied Graph Convolutional Network (GCN) on document-level graph to capture dependency structure information and utilized a variational selector network to determine the final selection probability of each word in a phrase, which relies on its probabilities of copying from a given document and being generated from a vocabulary.
Abstract: Keyphrase generation is an important fundamental task of natural language processing, which can help users quickly obtain valuable information from a large number of documents especially when they are facing with informal social media text. Existing Recurrent Neural Network (RNN) based keyphrase generation approaches cannot properly model the dependency structure of the informal text, which is often implicit between those distant words and plays an important role in extracting salient information. To obtain core features of text, we apply Graph Convolutional Network (GCN) on document-level graph to capture dependency structure information. The GCN-based node representations are further fed into a predictor network to provide potential candidates for copying mechanism. Moreover, we utilize a novel variational selector network to determine the final selection probability of each word in a phrase, which relies on its probabilities of copying from a given document and being generated from a vocabulary. Eventually, we introduce an enhancement mechanism to maximize the mutual information between document and generated keyphrase, thus ensuring the consistency between them. Experiment results show that our model outperforms previous state-of-the-art baselines on three social datasets, including Weibo, Twitter and StackExchange.
4 citations
TL;DR: Zhang et al. as discussed by the authors proposed a probabilistic keyphrase generation model from copy and generating spaces, which is built upon the vanilla variational encoder-decoder (VED) framework.
Abstract: Keyphrase generation is one of the most fundamental tasks in natural language processing (NLP). Most existing works on keyphrase generation mainly focus on using holistic distribution to optimize the negative log-likelihood loss, but they do not directly manipulate the copy and generating spaces, which may reduce the generability of the decoder. Additionally, existing keyphrase models are either unable to determine the dynamic numbers of keyphrases or produce the number of keyphrases implicitly. In this article, we propose a probabilistic keyphrase generation model from copy and generating spaces. The proposed model is built upon the vanilla variational encoder-decoder (VED) framework. On top of VED, two separate latent variables are adopted to model the distribution of data within the latent copy and generating spaces, respectively. Specifically, we adopt a von Mises-Fisher (vMF) distribution to obtain a condensed variable for modifying the generating probability distribution over the predefined vocabulary. Meanwhile, we utilize a clustering module, which is designed to promote Gaussian Mixture learning and subsequently extract a latent variable for the copy probability distribution. Moreover, we utilize a natural property of the Gaussian mixture network and use the number of filtered components to determine the number of keyphrases. The approach is trained based on latent variable probabilistic modeling, neural variational inference, and self-supervised learning. Experiments on social media and scientific article datasets outperform the state-of-the-art baselines in generating accurate predictions and controllable keyphrase numbers.
29 May 2012
TL;DR: This work proposes a consensus measure of fuzzy preference relation by computing the distance between the original collective judgment and the optimal collective estimation and shows that the optimal FPRs are helpful in measuring the consistency degree of individual judgment and of collective judgment.
Abstract: We study the consensus measure of fuzzy preference relation (FPR) based on multiplicative consistency. We propose a consensus measure by computing the distance between the original collective judgment and the optimal collective estimation. We show that the optimal FPRs are helpful in measuring the consistency degree of individual judgment and the consensus degree of collective judgment.
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01 Jan 2023
TL;DR: The authors proposed a deep neural network architecture based on word embedding and a bidirectional Long Short-Term Memory Recurrent Neural Network (Bi-LTSM) for keyphrase extraction.
Abstract: The exponential growth of textual data makes it challenging to retrieve pertinent information. Numerous methods for automating keyphrase extraction have emerged from earlier studies. Keyphrases have been used extensively to analyze, organize, and retrieve text content across various domains. Previous works have yielded numerous viable strategies for automated keyphrase extraction. They rely on domain-specific knowledge and features and select and rank the most relevant keyphrases. In this paper, we propose a deep neural network architecture based on word embedding and a Bidirectional Long Short-Term Memory Recurrent Neural Network “Bi-LTSM”. This architecture can capture the hidden context and the main topics of the document. Experimental analysis of benchmark datasets reveals that our proposed model achieves noteworthy performance compared to baselines and previous approaches for keyphrase extraction.
Peer Review•
TL;DR: This paper makes a contribution to the performance evaluation of 12 alternative classification strategies on datasets of breast cancer, and the right explanations for the classifiers' dominance were investigated.
Abstract: Received Jun 10, 2022 Revised Aug 13, 2022 Accepted Oct 6, 2022 COVID-19 has caused disruptions to many aspects of everyday life. To reduce the impact of this pandemic, its spreading must be controlled via face mask wearing. Manually mask-checking for everybody is embarrassing and uncontrollable. Hence, the proposed technique is used to help for automatic mask-checking based on deep learning platforms with real-time surveillance live infra-red (IR) camera. In this paper, two recent object detection platforms, named, you only look once version 3 (YOLOv3) and TensorFlow lite are adopted to accomplish this task. The two models are trained with a dataset consisting of images of persons with/without masks. This work is simulated with Google Colab then tested in real-time on an embedded device mated with fast GPU called Raspberry Pi 4 model B, 8 GB RAM. A comparison is made between the two models to verify their performance in relation to their precision rate and processing time. The work of this paper is also succeeded to realize multiple face masks real-time detection up to 10 facemasks in a single scene with high inference speed. Temperature is also measured using IR touchless sensor for each person with sound alarming to alert fever. The presented detector is cheap, light, small, and fast, with 99% accuracy rate during training and testing.