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Found 25 papers, 11 papers with code
基于词信息嵌入的汉语构词结构识别研究(Chinese Word-Formation Prediction based on Representations of Word-Related Features)
“作为一种意合型语言, 汉语中的构词结构刻画了构词成分之间的组合关系, 是认知、理解词义的关键。在中文信息处理领域, 此前的构词结构识别工作大多沿用句法层面的粗粒度标签, 且主要基于上下文等词间信息建模, 忽略了语素义、词义等词内信息对构词结构识别的作用。本文采用语言学视域下的构词结构标签体系, 构建汉语构词结构及相关信息数据集, 提出了一种基于Bi-LSTM和Self-attention的模型, 以此来探究词内、词间等多方面信息对构词结构识别的潜在影响和能达到的性能。实验取得了良好的预测效果, 准确率77. 87%, F1值78. 36%;同时, 对比测试揭示, 词内的语素义信息对构词结构识别具有显著的贡献, 而词间的上下文信息贡献较弱且带有较强的不稳定性。该预测方法与数据集, 将为中文信息处理的多种任务, 如语素和词结构分析、词义识别与生成、语言文字研究与词典编纂等提供新的观点和方案。”
Leveraging Word-Formation Knowledge for Chinese Word Sense Disambiguation
In this paper, we propose to leverage word-formation knowledge to enhance Chinese WSD.
阅读分级相关研究综述(A Survey of Leveled Reading)
“阅读分级的概念在二十世纪早期就被教育工作者提出, 随着人们对阅读变得越来越重视, 阅读分级引起了越来越多的关注, 自动阅读分级技术也得到了一定程度的发展。本文总结了近年来的阅读分级领域的研究进展, 首先介绍了阅读分级现有的标准和随之而产生的各种体系和语料资源。在此基础之上整理了在自动阅读分级工作已经广泛应用的三类方法:公式法、传统的机器学习方法和最近热门的深度学习方法, 并结合实验结果梳理了三类方法存在的弊利, 以及可以改进的方向。最后本文还对阅读分级的未来发展方向以及可以应用的领域进行了总结和展望。”
Cross-Lingual Leveled Reading Based on Language-Invariant Features
Specifically, we focus on adversarial training and cross-lingual pre-training method to transfer the LR knowledge learned from annotated data in the resource-rich English language to Chinese.
SiGeo: Sub-One-Shot NAS via Information Theory and Geometry of Loss Landscape
Neural Architecture Search (NAS) has become a widely used tool for automating neural network design.
Stochastic Biological System-of-Systems Modelling for iPSC Culture
Large-scale manufacturing of induced pluripotent stem cells (iPSCs) is essential for cell therapies and regenerative medicines.
Stochastic Molecular Reaction Queueing Network Modeling for In Vitro Transcription Process
To facilitate a rapid response to pandemic threats, this paper focuses on developing a mechanistic simulation model for in vitro transcription (IVT) process, a crucial step in mRNA vaccine manufacturing.
Structure-Function Dynamics Hybrid Modeling: RNA Degradation
RNA structure and functional dynamics play fundamental roles in controlling biological systems.
Compressed domain vibration detection and classification for distributed acoustic sensing
Distributed acoustic sensing (DAS) is a novel enabling technology that can turn existing fibre optic networks to distributed acoustic sensors.
Transformer-based Text Classification on Unified Bangla Multi-class Emotion Corpus
In this research, we propose a complete set of approaches for identifying and extracting emotions from Bangla texts.
Variance Reduction based Experience Replay for Policy Optimization
For reinforcement learning on complex stochastic systems where many factors dynamically impact the output trajectories, it is desirable to effectively leverage the information from historical samples collected in previous iterations to accelerate policy optimization.
Variance Reduction based Partial Trajectory Reuse to Accelerate Policy Gradient Optimization
Built on our previous study on green simulation assisted policy gradient (GS-PG) focusing on trajectory-based reuse, in this paper, we consider infinite-horizon Markov Decision Processes and create a new importance sampling based policy gradient optimization approach to support dynamic decision making.
Sequential Importance Sampling for Hybrid Model Bayesian Inference to Support Bioprocess Mechanism Learning and Robust Control
Driven by the critical needs of biomanufacturing 4. 0, we introduce a probabilistic knowledge graph hybrid model characterizing the risk- and science-based understanding of bioprocess mechanisms.
Opportunities of Hybrid Model-based Reinforcement Learning for Cell Therapy Manufacturing Process Control
Driven by the key challenges of cell therapy manufacturing, including high complexity, high uncertainty, and very limited process observations, we propose a hybrid model-based reinforcement learning (RL) to efficiently guide process control.
Is "My Favorite New Movie" My Favorite Movie? Probing the Understanding of Recursive Noun Phrases
We introduce the Recursive Noun Phrase Challenge (RNPC), a dataset of three textual inference tasks involving textual entailment and event plausibility comparison, precisely targeting the understanding of recursive NPs.
Variance Reduction based Experience Replay for Policy Optimization
For reinforcement learning on complex stochastic systems, it is desirable to effectively leverage the information from historical samples collected in previous iterations to accelerate policy optimization.
Decompose, Fuse and Generate: A Formation-Informed Method for Chinese Definition Generation
In this paper, we tackle the task of Definition Generation (DG) in Chinese, which aims at automatically generating a definition for a word.
Reinforcement Learning Assisted Oxygen Therapy for COVID-19 Patients Under Intensive Care
We developed a machine learning algorithm, based on a deep Reinforcement Learning (RL), for continuous management of oxygen flow rate for critical ill patients under intensive care, which can identify the optimal personalized oxygen flow rate with strong potentials to reduce mortality rate relative to the current clinical practice.
Policy Optimization in Dynamic Bayesian Network Hybrid Models of Biomanufacturing Processes
Biopharmaceutical manufacturing is a rapidly growing industry with impact in virtually all branches of medicines.
Incorporating Connections Beyond Knowledge Embeddings: A Plug-and-Play Module to Enhance Commonsense Reasoning in Machine Reading Comprehension
Conventional Machine Reading Comprehension (MRC) has been well-addressed by pattern matching, but the ability of commonsense reasoning remains a gap between humans and machines.
Personalized Multimorbidity Management for Patients with Type 2 Diabetes Using Reinforcement Learning of Electronic Health Records
Comorbid chronic conditions are common among people with type 2 diabetes.
Inductively Representing Out-of-Knowledge-Graph Entities by Optimal Estimation Under Translational Assumptions
Conventional Knowledge Graph Completion (KGC) assumes that all test entities appear during training.
Green Simulation Assisted Reinforcement Learning with Model Risk for Biomanufacturing Learning and Control
Biopharmaceutical manufacturing faces critical challenges, including complexity, high variability, lengthy lead time, and limited historical data and knowledge of the underlying system stochastic process.
Reinforcement Learning for Market Making in a Multi-agent Dealer Market
Market makers play an important role in providing liquidity to markets by continuously quoting prices at which they are willing to buy and sell, and managing inventory risk.
Global-Local Metamodel Assisted Two-Stage Optimization via Simulation
To integrate strategic, tactical and operational decisions, the two-stage optimization has been widely used to guide dynamic decision making.
