Incorporating rich syntax information in Grammatical Error Correction

Syntax parse trees are a method of representing sentence structure and are often used to provide models with syntax information and enhance downstream task performance. Because grammar and syntax are inherently linked, the incorporation of syntax parse trees in GEC is a natural solution. In this work, we present a method of incorporating syntax parse trees for Grammatical Error Correction (GEC). Building off a strong sequence-to-sequence Transformer baseline, we present a unified parse integration method for GEC that allows for the use of both dependency and constituency parse trees, as well as their combination - a syntactic graph. Specifically, on the sentence encoder, we propose a graph encoder that can encode dependency trees and constituent trees at the same time, yielding two representations for terminal nodes (i.e., the token of the sentence) and non-terminal nodes. We next use two cross-attentions (NT-Cross-Attention and T-Cross-Attention) to aggregate these source syntactic representations to the target side for final corrections prediction. In addition to evaluating our models on the popular CoNLL-2014 Shared Task and JFLEG GEC benchmarks, we affirm the effectiveness of our proposed method by testing both varying levels of parsing quality and exploring the use of both parsing formalisms. With further empirical exploration and analysis to identify the source of improvement, we found that rich syntax information provided clear clues for GEC; a syntactic graph composed of multiple syntactic parse trees can effectively compensate for the limited quality and insufficient error correction capability of a single syntactic parse tree.     

Interest Evolution-driven Gated Neighborhood aggregation representation for dynamic recommendation in e-commerce

Recommender system as an effective method to reduce information overload has been widely used in the e-commerce field. Existing studies mainly capture semantic features by considering user-item interactions or behavioral history records, which ignores the sparsity of interactions and the drift of user preferences. To cope with these challenges, we introduce the recently popular Graph Neural Networks (GNN) and propose an Interest Evolution-driven Gated Neighborhood (IEGN) aggregation representation model which can capture accurate user representation and track the evolution of user interests. Specifically, in IEGN, we explicitly model the relational information between neighbor nodes by introducing the gated adaptive propagation mechanism. Then, a personalized time interval function is designed to track the evolution of user interests. In addition, a high-order convolutional pooling operation is used to capture the correlation among the short-term interaction sequence. The user preferences are predicted by the fusion of user dynamic preferences and short-term interaction features. Extensive experiments on Amazon and Alibaba datasets show that IEGN outperforms several state-of-the-art methods in recommendation tasks.     

Aspect sentiment analysis with heterogeneous graph neural networks

Aspect-based sentiment analysis technologies may be a very practical methodology for securities trading, commodity sales, movie rating websites, etc. Most recent studies adopt the recurrent neural network or attention-based neural network methods to infer aspect sentiment using opinion context terms and sentence dependency trees. However, due to a sentence often having multiple aspects sentiment representation, these models are hard to achieve satisfactory classification results. In this paper, we discuss these problems by encoding sentence syntax tree, words relations and opinion dictionary information in a unified framework. We called this method heterogeneous graph neural networks (Hete_GNNs). Firstly, we adopt the interactive aspect words and contexts to encode the sentence sequence information for parameter sharing. Then, we utilized a novel heterogeneous graph neural network for encoding these sentences’ syntax dependency tree, prior sentiment dictionary, and some part-of-speech tagging information for sentiment prediction. We perform the Hete_GNNs sentiment judgment and report the experiments on five domain datasets, and the results confirm that the heterogeneous context information can be better captured with heterogeneous graph neural networks. The improvement of the proposed method is demonstrated by aspect sentiment classification task comparison.     

基于图嵌入的特征提取算法研究

通过对图、完全图和正则图概念的介绍,详细地描述了图嵌入的方法,同时对主成分分析、线性鉴别分析、局部保持投影、保持近邻嵌入、L1图及其嵌入等经典的特征提取算法进行了详细的代数推导,列出了详细的推导过程,得出这些经典算法可以用图嵌入理论来解释的结论,最后得出特征提取算法的核心在于算法的图构造.     

方程、真值表与状态转换图在逻辑推理中的应用

给出了方程、真值表与状态转换图在逻辑推理中的应用。     

Petersen图类的边符号控制数

设图G=G(V,E),令函数f:E→{-1,1},f的权w(f)=∑x∈Ef[x],对x∈E中任一元素,定义f[x]=∑y∈N[x]f(y),这里N[x]表示E中x及其关联边的集合.图G的边符号控制函数为f:E→{-1,1},满足对所有的x∈E有f[x]≥1,图G的边符号控制数γS(G)就是图G上边符号控制数的最小权,称其f为图G的γS-函数.本文得到了Petersen图类的边符号控制数.     

多媒体教学绩效的二维有向图模型

针对目前高校中不同课程的多媒体教学绩效差异问题,提出一个二维有向图模型,用于描述采用不同多媒体教学手段与课程教学绩效的关系,目的是找出产生多媒体教学绩效的因素。利用二维有向图的三维关联矩阵计算课程教学绩效,并以课程为对象举例说明二维有向图的应用。     

OpenSceneGraph的大地形可视化方法研究

针对传统的地形海量数据(基于规则格网数字高程模型)渲染对软硬件需求高、开发周期长、开发难度大等问题,提出了基于Open Scene Graph的解决方案,使海量数据渲染在对硬件需求以及开发难度上都得到了明显的改善。     

思维导图——开启人类智慧的金钥匙

思维导图是英国脑力开发专家托尼·布赞于1970年代发明的一种有效使用大脑的方法。它是以关键词为节点，以流畅的线条为纽带而绘制的形象直观的图形图像，由于同时启动左脑和右脑的全部功能，是一种结构化的放射性思考模式，因而可广泛应用于阅读、记忆、激发联想与创意、项目规划与管理、会议、谈判等日常生活、学习和工作的各个领域。     

广义逆矩阵及其性质

本文给出一种广义逆矩阵的定义并指出了它的一些性质.