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1.
引入句法依存信息到原方面术语,提出一种新的方面术语表示方法,利用Glove词向量表示单词以及单词与单词之间的依存关系,构造出包含句法依存信息的依存关系邻接矩阵和依存关系表示矩阵,利用图卷积神经网络和多头注意力机制将句法依存信息融入到方面术语中,使得方面术语表达与上下文结构高度相关。将改进后的方面词术语表示替换到现有模型后,模型泛化能力得到有效提升。对比试验和分析结果表明:该方法具有有效性和泛化性。 相似文献
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《Ceramics International》2022,48(4):5066-5074
We studied the morphological nature of various thin films such as silicon carbide (SiC), diamond (C), germanium (Ge), and gallium nitride (GaN) on silicon substrate Si(100) using the pulsed laser deposition (PLD) method and Monte Carlo simulation. We, for the first time, systematically employed the visibility algorithm graph to meticulously study the morphological features of various PLD grown thin films. These thin-film morphologies are investigated using random distribution, Gaussian distribution, patterned heights, etc. The nature of the interfacial height of individual surfaces is examined by a horizontal visibility graph (HVG). It demonstrates that the continuous interfacial height of the silicon carbide, diamond, germanium, and gallium nitride films are attributed to random distribution and Gaussian distribution in thin films. However, discrete peaks are obtained in the brush and step-like morphology of germanium thin films. Further, we have experimentally verified the morphological nature of simulated silicon carbide, diamond, germanium, and gallium nitride thin films were grown on Si(100) substrate by pulsed laser deposition (PLD) at elevated temperature. Various characterization techniques have been used to study the morphological, and electrical properties which confirmed the different nature of the deposited films on the Silicon substrate. Decent hysteresis behavior has been confirmed by current-voltage (IV) measurement in all the four deposited films. The highest current has been measured for GaN at ~60 nA and the lowest current in SiC at ~30 nA level which is quite low comparing with the expected signal level (μA). The HVG technique is suitable to understand surface features of thin films which are substantially advantageous for the energy devices, detectors, optoelectronic devices operating at high temperatures. 相似文献
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随着人脸识别技术的不断进步以及人脸卡口的大范围且密集的部署, 本文针对团伙犯罪案件侦察这一应用场景, 对人脸卡口数据进行深入挖掘, 探究其中行人间的共现关系, 获取所关注的嫌疑人的现实社交网络, 锁定团伙其余人员. 经过实验比对和论证, 本文使用Chinese Whispers聚类算法对行人节点进行识别, 通过Faiss加速邻接边的构建, 加速图的初始化步骤, 解决其聚类效率低下的问题. 在此基础上, 使用共现频次和Apriori算法中的置信度挖掘行人间的共现关系, 构建行人共现关系图谱. 相似文献
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Aggregate question answering essentially returns answers for given questions by obtaining query graphs with unique dependencies between values and corresponding objects. Word order dependency, as the key to uniquely identify dependency of the query graph, reflects the dependencies between the words in the question. However, due to the semantic gap caused by the expression difference between questions encoded with word vectors and query graphs represented with logical formal elements, it is not trivial to match the correct query graph for the question. Most existing approaches design more expressive query graphs for complex questions and rank them just by directly calculating their similarities, ignoring the semantic gap between them. In this paper, we propose a novel Structure-sensitive Semantic Matching(SSM) approach that learns aligned representations of dependencies in questions and query graphs to eliminate their gap. First, we propose a cross-structure matching module to bridge the gap between two modalities(i.e., textual question and query graph). Then, we propose an entropy-based gated AQG filter to remove the structural noise caused by the uncertainty of dependencies. Finally, we present a two-channel query graph representation that fuses the semantics of abstract structure and grounding content of the query graph explicitly. Experimental results show that SSM could learn aligned representations of questions and query graphs to eliminate the gaps between their dependencies, and improves up to 12% (F1 score) on aggregation questions of two benchmark datasets. 相似文献
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In this paper, we present LinkingPark, an automatic semantic annotation system for tabular data to knowledge graph matching. LinkingPark is designed as a modular framework which can handle Cell-Entity Annotation (CEA), Column-Type Annotation (CTA), and Columns-Property Annotation (CPA) altogether. It is built upon our previous SemTab 2020 system, which won the 2nd prize among 28 different teams after four rounds of evaluations. Moreover, the system is unsupervised, stand-alone, and flexible for multilingual support. Its backend offers an efficient RESTful API for programmatic access, as well as an Excel Add-in for ease of use. Users can interact with LinkingPark in near real-time, further demonstrating its efficiency. 相似文献
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