High-extensible scene graph framework based on component techniques

INTRODUCTION With the tremendous growth of the digital en- tertainment industry in the past decades, the scale of game scene is becoming more gigantic. To effectively manage and organize the massive data, game engine usually uses hierarchical scene graph. Many popular 3D modelling tools, such as Maya, 3D Studio Max, use scene graph to manage the scene content (Sowizral, 2000). 3D modelling software—Houdini directly edits scene graph to create and modify the virtual environment. Most v…     

悲剧性的毁灭与喜剧性的忧郁——试比较《欲望号街车》与《樱桃园》不同的审美特质

作为深受俄国剧作家契诃夫影响的美国剧作家,田纳西.威廉斯在其剧作《欲望号街车》里表达了与契诃夫的名剧《樱桃园》极为相似的创作主题。然而,两位剧作家各自生活经历的不同,以及各自审美观念的不同,造成了这两部主题相近的戏剧有着不同的审美特质。威廉斯的悲剧性审美观照和契诃夫戏剧性的审美心态赋予了这两部主题近似的戏剧以相异的戏剧冲突处理方式,形成了两部戏不同的审美情趣,同时也体现了契诃夫与威廉斯两位剧作家不同的审美精神。     

RDA的实体及关系解析

FRBR的实体-关系模型是RDA编制的结构基础,这种概念完全不同于AACR2的理念,同时它也是理解RDA的难点论文主要针对RDA中的实体、实体中的元素、实体之间的关系进行了分析,并利用图表进行详细的说明.结论是：RDA对文献描述的四个层次包括作品、内容表达、具体表现、单件,可以对书目信息进行深度揭示,四个层次之间也存在比较复杂的关系.     

AdaSL: An Unsupervised Domain Adaptation framework for Arabic multi-dialectal Sequence Labeling

Dialectal Arabic (DA) refers to varieties of everyday spoken languages in the Arab world. These dialects differ according to the country and region of the speaker, and their textual content is constantly growing with the rise of social media networks and web blogs. Although research on Natural Language Processing (NLP) on standard Arabic, namely Modern Standard Arabic (MSA), has witnessed remarkable progress, research efforts on DA are rather limited. This is due to numerous challenges, such as the scarcity of labeled data as well as the nature and structure of DA. While some recent works have reached decent results on several DA sentence classification tasks, other complex tasks, such as sequence labeling, still suffer from weak performances when it comes to DA varieties with either a limited amount of labeled data or unlabeled data only. Besides, it has been shown that zero-shot transfer learning from models trained on MSA does not perform well on DA. In this paper, we introduce AdaSL, a new unsupervised domain adaptation framework for Arabic multi-dialectal sequence labeling, leveraging unlabeled DA data, labeled MSA data, and existing multilingual and Arabic Pre-trained Language Models (PLMs). The proposed framework relies on four key components: (1) domain adaptive fine-tuning of multilingual/MSA language models on unlabeled DA data, (2) sub-word embedding pooling, (3) iterative self-training on unlabeled DA data, and (4) iterative DA and MSA distribution alignment. We evaluate our framework on multi-dialectal Named Entity Recognition (NER) and Part-of-Speech (POS) tagging tasks.The overall results show that the zero-shot transfer learning, using our proposed framework, boosts the performance of the multilingual PLMs by 40.87% in macro-F1 score for the NER task, while it boosts the accuracy by 6.95% for the POS tagging task. For the Arabic PLMs, our proposed framework increases performance by 16.18% macro-F1 for the NER task and 2.22% accuracy for the POS tagging task, and thus, achieving new state-of-the-art zero-shot transfer learning performance for Arabic multi-dialectal sequence labeling.     

Multi-heterogeneous neighborhood-aware for Knowledge Graphs alignment

Entity alignment is an important task for the Knowledge Graph (KG) completion, which aims to identify the same entities in different KGs. Most of previous works only utilize the relation structures of KGs, but ignore the heterogeneity of relations and attributes of KGs. However, these information can provide more feature information and improve the accuracy of entity alignment. In this paper, we propose a novel Multi-Heterogeneous Neighborhood-Aware model (MHNA) for KGs alignment. MHNA aggregates multi-heterogeneous information of aligned entities, including the entity name, relations, attributes and attribute values. An important contribution is to design a variant attention mechanism, which adds the feature information of relations and attributes to the calculation of attention coefficients. Extensive experiments on three well-known benchmark datasets show that MHNA significantly outperforms 12 state-of-the-art approaches, demonstrating that our approach has good scalability and superiority in both cross-language and monolingual KGs. An ablation study further supports the effectiveness of our variant attention mechanism.     

Relevance-based entity selection for ad hoc retrieval

Recent developments have shown that entity-based models that rely on information from the knowledge graph can improve document retrieval performance. However, given the non-transitive nature of relatedness between entities on the knowledge graph, the use of semantic relatedness measures can lead to topic drift. To address this issue, we propose a relevance-based model for entity selection based on pseudo-relevance feedback, which is then used to systematically expand the input query leading to improved retrieval performance. We perform our experiments on the widely used TREC Web corpora and empirically show that our proposed approach to entity selection significantly improves ad hoc document retrieval compared to strong baselines. More concretely, the contributions of this work are as follows: (1) We introduce a graphical probability model that captures dependencies between entities within the query and documents. (2) We propose an unsupervised entity selection method based on the graphical model for query entity expansion and then for ad hoc retrieval. (3) We thoroughly evaluate our method and compare it with the state-of-the-art keyword and entity based retrieval methods. We demonstrate that the proposed retrieval model shows improved performance over all the other baselines on ClueWeb09B and ClueWeb12B, two widely used Web corpora, on the [email protected], and [email protected] metrics. We also show that the proposed method is most effective on the difficult queries. In addition, We compare our proposed entity selection with a state-of-the-art entity selection technique within the context of ad hoc retrieval using a basic query expansion method and illustrate that it provides more effective retrieval for all expansion weights and different number of expansion entities.     

方志类古籍地名识别及系统构建

以地方志资料汇编<方志物产>(广东分卷)为语料,设计并构建了古籍地名识别系统.采用规则与统计相结合的命名实体识别方法,实现了物产地名的自动识别.分析了命名实体识别技术在中国方志类古籍整理中的应用前景,为方志类古籍进行数字化整理、挖掘物产分布、物产引进和传播等相关研究提供了新的途径.     

自学考试统一信息管理平台的研究与实现

基于微软平台推出的两项新技术ASP.NET MVC、Entity Framework（或称实体框架）和经典三层架构技术的研究,选择管理平台实现所需的技术基础,合理设计管理平台的系统架构,选择功能强大的开发环境和实用工具组件提高工作效率和系统性能稳定性,综合考虑并设计系统其他方面,实现自学考试统一信息管理平台Web应用程序,提高自学考试信息管理效率和服务功能。     

Contextual feature selection for text classification

We present a simple approach for the classification of “noisy” documents using bigrams and named entities. The approach combines conventional feature selection with a contextual approach to filter out passages around selected features. Originally designed for call for tender documents, the method can be useful for other web collections that also contain non-topical contents. Experiments are conducted on our in-house collection as well as on the 4-Universities data set, Reuters 21578 and 20 Newsgroups. We find a significant improvement on our collection and the 4-Universities data set (10.9% and 4.1%, respectively). Although the best results are obtained by combining bigrams and named entities, the impact of the latter is not found to be significant.     

论田纳西·威廉斯“诗化现实主义”的成熟——《玻璃动物园》和《欲望号街车》中的象征主义和表现主义之比较研究

田纳西·威廉斯开创的"诗化现实主义"的创作方法是其跻身于世界级戏剧大师的一个不可或缺的重要因素.这一创作方法的成熟经历了一个漫长的反复试验过程.<欲望号街车>是其成熟的标志.文章通过详细地比较<玻璃动物园>和<欲望号街车>中象征主义和表现主义创作手法的运用及其交融的程度,来考察其"诗化现实主义"的创作方法的成熟这一特定阶段.