面向小语种机器翻译的平行语料库构建方法

神经机器翻译模型的训练效果在很大程度上取决于平行语料库的规模和质量。除了一些常见语言外,汉语与小语种间高质量平行语料库的建设一直处于滞后状态。现有小语种平行语料库多采用自动句子对齐技术利用网络资源构建而成,在文本质量和领域等方面有诸多局限性。采用人工翻译的方式可以构建高质量平行语料库,但是缺乏相关经验和方法。文中从机器翻译实践者和研究者角度出发,介绍了经济高效的人工构建小语种平行语料库的工作,包括其总体目标、实施过程、流程细节和最后结果。在构建过程中尝试并积累了各种经验,形成了小语种到汉语平行语料库构建方法、建议的总结。最终,成功构建了波斯语到汉语、印地语到汉语、印度尼西亚语到汉语各50万条高质量平行语料。实验结果表明,所构建的平行语料库有较好的质量,提高了小语种神经机器翻译模型的训练效果。     

基于UG的采煤机防爆盖板三维标注及数控工艺研究

随着三维CAD技术的发展,产品制造信息在三维模型和二维图纸之间的反复传递,严重影响了产品制造信息的正确、快速传递。基于三维产品制造信息技术,以采煤机防爆盖板为例,在UG NX10.0中建立了防爆盖板的三维模型并进行了三维标注,设计了加工工艺路线,规划了数控加工刀具轨迹,进行了加工仿真,验证了数控加工的正确性,在此基础上完成了采煤机防爆盖板的三维工艺式设计。该研究对基于模型定义的数字化设计与制造研究,实现制造业信息化具有重要意义。     

Building a text collection for Urdu information retrieval

Urdu is a widely spoken language in the Indian subcontinent with over 300 million speakers worldwide. However, linguistic advancements in Urdu are rare compared to those in other European and Asian languages. Therefore, by following Text Retrieval Conference standards, we attempted to construct an extensive text collection of 85 304 documents from diverse categories covering over 52 topics with relevance judgment sets at 100 pool depth. We also present several applications to demonstrate the effectiveness of our collection. Although this collection is primarily intended for text retrieval, it can also be used for named entity recognition, text summarization, and other linguistic applications with suitable modifications. Ours is the most extensive existing collection for the Urdu language, and it will be freely available for future research and academic education.     

Semantic content-based image retrieval: A comprehensive study

The complexity of multimedia contents is significantly increasing in the current digital world. This yields an exigent demand for developing highly effective retrieval systems to satisfy human needs. Recently, extensive research efforts have been presented and conducted in the field of content-based image retrieval (CBIR). The majority of these efforts have been concentrated on reducing the semantic gap that exists between low-level image features represented by digital machines and the profusion of high-level human perception used to perceive images. Based on the growing research in the recent years, this paper provides a comprehensive review on the state-of-the-art in the field of CBIR. Additionally, this study presents a detailed overview of the CBIR framework and improvements achieved; including image preprocessing, feature extraction and indexing, system learning, benchmarking datasets, similarity matching, relevance feedback, performance evaluation, and visualization. Finally, promising research trends, challenges, and our insights are provided to inspire further research efforts.     

“三规合一”数据的注记图层坐标转换处理方法研究

注记图层因为存储了文字的位置信息,且具有相对比例尺,字体大小可以相对变化,越来越多地被应用于GIS数据建库中。 ArcGIS MDB坐标转换中,注记图层的处理不同于普通的点线面图层。本文详细讲述了＂三规合一＂数据坐标转换中注记图层的处理方法和过程,通过本方法进行处理,既省略了重建注记子类型分类的步骤,减少手工录入量,又保留了原有图层各属性字段内容,同时也保留了注记字体、大小、角度等相关显示信息,能最大程度保持原有注记显示效果。     

Large scale automatic image annotation based on convolutional neural network

Automatic image annotation is one of the most important challenges in computer vision, which is critical to many real-world researches and applications. In this paper, we focus on the issue of large scale image annotation with deep learning. Firstly, considering the existing image data, especially the network images, most of the labels of themselves are inaccurate or imprecise. We propose a Multitask Voting (MV) method, which can improve the accuracy of original annotation to a certain extent, thereby enhancing the training effect of the model. Secondly, the MV method can also achieve the adaptive label, whereas most existing methods pre-specify the number of tags to be selected. Additionally, based on convolutional neural network, a large scale image annotation model MVAIACNN is constructed. Finally, we evaluate the performance with experiments on the MIRFlickr25K and NUS-WIDE datasets, and compare with other methods, demonstrating the effectiveness of the MVAIACNN.     

一种基于小字典不对等语料的跨语言词嵌入方法

双语词嵌入通常采用从源语言空间到目标语言空间映射,通过源语言映射嵌入到目标语言空间的最小距离线性变换实现跨语言词嵌入。然而大型的平行语料难以获得,词嵌入的准确率难以提高。针对语料数量不对等、双语语料稀缺情况下的跨语言词嵌入问题,该文提出一种基于小字典不对等语料的跨语言词嵌入方法,首先对单语词向量进行归一化,对小字典词对正交最优线性变换求得梯度下降初始值,然后通过对大型源语言(英语)语料进行聚类,借助小字典找到与每一聚类簇相对应的源语言词,取聚类得到的每一簇词向量均值和源语言与目标语言对应的词向量均值,建立新的双语词向量对应关系,将新建立的双语词向量扩展到小字典中,使得小字典得以泛化和扩展。最后,利用泛化扩展后的字典对跨语言词嵌入映射模型进行梯度下降求得最优值。在英语—意大利语、德语和芬兰语上进行了实验验证,实验结果证明该文方法可以在跨语言词嵌入中减少梯度下降迭代次数,减少训练时间,同时在跨语言词嵌入上表现出较好的正确率。     

Enhancing Virtual Reference: Techniques and Technologies to Engage Users and Enrich Interaction

Virtual reference service has become ubiquitous in academic libraries. The increased adoption of chat technology brings to light some of the challenges of providing high-quality reference service. The lack of a personal connection, increased transaction time, and technical problems are often cited. Techniques, such as conducting a reference interview or using online communication conventions, can make virtual reference as effective as face-to-face reference. Additionally, inclusion of video or Web annotation in the virtual reference session can add an element of personalization that might otherwise be missing. Virtual reference can be an emotionally rich, engaging, and effective form of reference service if these techniques and technologies are adopted.     

高正确率的双语语块对齐算法研究

高质量的自动对齐双语语块,对于机器翻译系统,特别是计算机辅助翻译系统的性能提高有重要作用,而且对于人工翻译以及辞典编纂也都有巨大的应用价值。该文提出基于单词间粘合度与松弛度的语块划分评分方法以及双语语块划分的双向约束算法,使得源语言和目标语言的语块的划分与对齐能相互促进。与传统方法相比,因为无需事先进行双语语块划分,而是在搜索最佳对齐时动态地考察划分效果,故可以减少边界划分错误对对齐结果的影响。该算法获得了远超过传统算法的高正确率。