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Found 7 papers, 0 papers with code
回避类动词的句法语义(The Syntax and Semantics of Verbs of Avoiding)
“回避行为是人类重要的认知经验, 己有对回避类动词的研究多为分析回避类动词的隐性否定语义和语篇博弈效果, 但对该类动词的深层句法和语义分析不多。本文选取五个双音节回避类动词为研究对象, 利用依存语法的相关理论, 基于大规模语料分析回避类动词的句法和语义特征, 从而深化对该类动词的研究。本研究的结果也可以进一步完善现有的汉语词典。本研究对汉语研究、汉语教学、词典编纂等具有重要的参考价值。”
基于依存语法的偷抢类动词研究(Research of Verbs of Stealing and Robbing Based on Dependency Grammar)
“本文筛选了汉语“偷抢”类动词的单句, 并借助依存语法的标注体系对“偷抢”类动词句法依存和语义依存进行定量分析。研究结果表明, 当汉语“偷抢”类动词为从属词时, 表现出句法功能的多样性、内部相似性和区别其他动词小类的特异性, 其语义角色分布具有多样性。当汉语“偷抢”类动词为支配词时, 该类动词的句法依存随其不同的句法功能而发生变化;从该类动词的语义依存来看, 其客体语义密度整体低于主体语义密度, 最常见的情境角色是地点和时间, 在事件关系中, 并列事件发生概率最高。“偷抢”类动词的句法语义特点丰富, 主要的句型为主谓宾句式, 而该句式中最常用的语义搭配模式是施事对受事实施偷抢动作。本研究结合依存语法和框架语义学, 深化了对汉语“偷抢”类动词的句法、语义和事件关系的了解, 促进了对该类动词的研究。”
SparseAD: Sparse Query-Centric Paradigm for Efficient End-to-End Autonomous Driving
End-to-End paradigms use a unified framework to implement multi-tasks in an autonomous driving system.
A Consumer-tier based Visual-Brain Machine Interface for Augmented Reality Glasses Interactions
Here we develop a consumer-tier Visual-Brain Machine Inteface(V-BMI) system specialized for Augmented Reality(AR) glasses interactions.
PSNet: a deep learning model based digital phase shifting algorithm from a single fringe image
As the gold standard for phase retrieval, phase-shifting algorithm (PS) has been widely used in optical interferometry, fringe projection profilometry, etc.
CLTS+: A New Chinese Long Text Summarization Dataset with Abstractive Summaries
In order to solve this problem, we paraphrase the reference summaries in CLTS, the Chinese Long Text Summarization dataset, correct errors of factual inconsistencies, and propose the first Chinese Long Text Summarization dataset with a high level of abstractiveness, CLTS+, which contains more than 180K article-summary pairs and is available online.
Structure Information is the Key: Self-Attention RoI Feature Extractor in 3D Object Detection
With the newly introduced SARFE, we improve the performance of the state-of-the-art 3D detectors by a large margin in cyclist on KITTI dataset while keeping real-time capability.
