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Found 31 papers, 8 papers with code
基于新闻图式结构的篇章功能语用识别方法(Discourse Functional Pragmatics Recognition Based on News Schemata)
“篇章分析是自然语言处理领域的研究热点和重点, 篇章功能语用研究旨在分析篇章单元在篇章中的功能和作用, 有助于深入理解篇章的主题和内容。目前篇章分析研究以形式语法为主, 而篇章作为一个整体的语义单位, 其功能和语义却没有引起足够重视。已有功能语用研究以面向事件抽取任务为主, 并未进行通用领域的功能语用研究。鉴于功能语用研究的重要性和研究现状, 本文提出了基于新闻图式结构的篇章功能语用识别方法来识别篇章功能语用。该方法在获取段落交互信息的同时又融入了篇章的新闻图式结构信息, 并结合段落所在篇章中的位置信息, 从而有效地提高了篇章功能语用的识别能力。在汉语宏观篇章树库的实验结果证明, 本文提出的方法优于所有基准系统。”
Automated Chinese Essay Scoring from Multiple Traits
Automatic Essay Scoring (AES) is the task of using the computer to evaluate the quality of essays automatically.
Key Mention Pairs Guided Document-Level Relation Extraction
To this end, we propose a novel DocRE model called Key Mention pairs Guided Relation Extractor (KMGRE) to directly model mention-level relations, containing two modules: a mention-level relation extractor and a key instance classifier.
Not Just Classification: Recognizing Implicit Discourse Relation on Joint Modeling of Classification and Generation
Therefore, we first view IDRR as a generation task and further propose a method joint modeling of the classification and generation.
Read Pointer Meters in complex environments based on a Human-like Alignment and Recognition Algorithm
Recently, developing an automatic reading system for analog measuring instruments has gained increased attention, as it enables the collection of numerous state of equipment.
Image Compressed Sensing Using Non-local Neural Network
In this paper, a novel image CS framework using non-local neural network (NL-CSNet) is proposed, which utilizes the non-local self-similarity priors with deep network to improve the reconstruction quality.
Residual Relaxation for Multi-view Representation Learning
Multi-view methods learn representations by aligning multiple views of the same image and their performance largely depends on the choice of data augmentation.
ReTraCk: A Flexible and Efficient Framework for Knowledge Base Question Answering
We present Retriever-Transducer-Checker (ReTraCk), a neural semantic parsing framework for large scale knowledge base question answering (KBQA).
Ranked #1 on
Knowledge Base Question Answering
on GrailQA
Detecting and Correcting IMU Movements During Joint Angle Estimation
The results demonstrate our method is a promising solution to detecting and correcting IMU movements during JAE.
Continuous Prediction of Lower-Limb Kinematics From Multi-Modal Biomedical Signals
Challenges of exoskeletons motor intent decoding schemes remain in making a continuous prediction to compensate for the hysteretic response caused by mechanical transmission.
Multi-Stage Residual Hiding for Image-into-Audio Steganography
The widespread application of audio communication technologies has speeded up audio data flowing across the Internet, which made it a popular carrier for covert communication.
Chinese Paragraph-level Discourse Parsing with Global Backward and Local Reverse Reading
Discourse structure tree construction is the fundamental task of discourse parsing and most previous work focused on English.
Hierarchical Complementary Learning for Weakly Supervised Object Localization
The aim of removing parts from image or detected parts of the object is to force the model to detect the other features.
Entropy Guided Adversarial Model for Weakly Supervised Object Localization
Unfortunately, the network activates only the features that discriminate the object and does not activate the whole object.
Deep-VFX: Deep Action Recognition Driven VFX for Short Video
In the application, short-form mobile video is so popular all over the world such as Tik Tok.
Improving Entity Linking by Modeling Latent Entity Type Information
Existing state of the art neural entity linking models employ attention-based bag-of-words context model and pre-trained entity embeddings bootstrapped from word embeddings to assess topic level context compatibility.
Ranked #2 on
Entity Disambiguation
on AIDA-CoNLL
(Micro-F1 metric)
Enhancing Neural Data-To-Text Generation Models with External Background Knowledge
Recent neural models for data-to-text generation rely on massive parallel pairs of data and text to learn the writing knowledge.
On Compression of Unsupervised Neural Nets by Pruning Weak Connections
Unsupervised neural nets such as Restricted Boltzmann Machines(RBMs) and Deep Belif Networks(DBNs), are powerful in automatic feature extraction, unsupervised weight initialization and density estimation.
Convolutional Neural Networks based Intra Prediction for HEVC
To overcome this problem, in this paper, an intra prediction convolutional neural network (IPCNN) is proposed for intra prediction, which exploits the rich context of the current block and therefore is capable of improving the accuracy of predicting the current block.
MCDTB: A Macro-level Chinese Discourse TreeBank
In view of the differences between the annotations of micro and macro discourse rela-tionships, this paper describes the relevant experiments on the construction of the Macro Chinese Discourse Treebank (MCDTB), a higher-level Chinese discourse corpus.
Joint Modeling of Structure Identification and Nuclearity Recognition in Macro Chinese Discourse Treebank
Discourse parsing is a challenging task and plays a critical role in discourse analysis.
Deep neural network based sparse measurement matrix for image compressed sensing
In this paper, a Deep neural network based Sparse Measurement Matrix (DSMM) is learned by the proposed convolutional network to reduce the sampling computational complexity and improve the CS reconstruction performance.
An efficient deep convolutional laplacian pyramid architecture for CS reconstruction at low sampling ratios
To address this problem, we propose a deep convolutional Laplacian Pyramid Compressed Sensing Network (LapCSNet) for CS, which consists of a sampling sub-network and a reconstruction sub-network.
Ranked #1 on
Compressive Sensing
on Set5
An End-to-End Compression Framework Based on Convolutional Neural Networks
The second CNN, named reconstruction convolutional neural network (RecCNN), is used to reconstruct the decoded image with high-quality in the decoding end.
Deep Networks for Compressed Image Sensing
First of all, we train a sampling matrix via the network training instead of using a traditional manually designed one, which is much appropriate for our deep network based reconstruct process.
Single Image Super-Resolution with Dilated Convolution based Multi-Scale Information Learning Inception Module
With the novel dilated convolution based inception module, the proposed end-to-end single image super-resolution network can take advantage of multi-scale information to improve image super-resolution performance.
Ranked #45 on
Image Super-Resolution
on Set14 - 4x upscaling
Efficient Divide-And-Conquer Classification Based on Feature-Space Decomposition
Firstly, we divide the feature space into several subspaces using the decomposition method proposed in this paper.
Spatially Directional Predictive Coding for Block-based Compressive Sensing of Natural Images
At the encoder, for each block of compressive sensing (CS) measurements, the optimal pre-diction is selected from a set of prediction candidates that are generated by four designed directional predictive modes.
Structural Group Sparse Representation for Image Compressive Sensing Recovery
Compressive Sensing (CS) theory shows that a signal can be decoded from many fewer measurements than suggested by the Nyquist sampling theory, when the signal is sparse in some domain.
