外贸函电的语篇功能分析

对外贸函电语篇的分析有助于理解外贸信函的写作方法,更好地开展信函沟通。本文从语篇功能的主述位结构、信息结构及衔接三个方面对外贸函电的语篇功能进行分析,探讨外贸函电的语法和词汇运用。     

Neural embedding-based indices for semantic search

Traditional information retrieval techniques that primarily rely on keyword-based linking of the query and document spaces face challenges such as the vocabulary mismatch problem where relevant documents to a given query might not be retrieved simply due to the use of different terminology for describing the same concepts. As such, semantic search techniques aim to address such limitations of keyword-based retrieval models by incorporating semantic information from standard knowledge bases such as Freebase and DBpedia. The literature has already shown that while the sole consideration of semantic information might not lead to improved retrieval performance over keyword-based search, their consideration enables the retrieval of a set of relevant documents that cannot be retrieved by keyword-based methods. As such, building indices that store and provide access to semantic information during the retrieval process is important. While the process for building and querying keyword-based indices is quite well understood, the incorporation of semantic information within search indices is still an open challenge. Existing work have proposed to build one unified index encompassing both textual and semantic information or to build separate yet integrated indices for each information type but they face limitations such as increased query process time. In this paper, we propose to use neural embeddings-based representations of term, semantic entity, semantic type and documents within the same embedding space to facilitate the development of a unified search index that would consist of these four information types. We perform experiments on standard and widely used document collections including Clueweb09-B and Robust04 to evaluate our proposed indexing strategy from both effectiveness and efficiency perspectives. Based on our experiments, we find that when neural embeddings are used to build inverted indices; hence relaxing the requirement to explicitly observe the posting list key in the indexed document: (a) retrieval efficiency will increase compared to a standard inverted index, hence reduces the index size and query processing time, and (b) while retrieval efficiency, which is the main objective of an efficient indexing mechanism improves using our proposed method, retrieval effectiveness also retains competitive performance compared to the baseline in terms of retrieving a reasonable number of relevant documents from the indexed corpus.     

休闲体育消费的若干理论问题

采用文献资料、逻辑推理等研究方法,以休闲消费和精神消费理论为基础,对休闲体育消费的内涵、分类、特征、性质、功能、影响因素和发展策略进行初步的探讨。     

论幼儿园课程中教师的作用

对幼儿教师在幼儿园课程中作用的认识旨在追问幼儿教师自身的角色自明及其职业自有的责任。对幼儿教师在幼儿园课程中的作用,主要有三方面认识：（1）在幼儿园课程体系生成和设计中的作用;（2）在课程具体实施中的作用;（3）在课程评价和维护中的作用。     

论儿童文学促进幼儿情感发展及其优化策略

幼儿期是人的情感发展的关键期和敏感期,在此期间对幼儿进行儿童文学教育意义重大.本文首先论述了儿童文学促进幼儿情感发展的情育、催化、取向等三个方面的独特功能.然后论述了儿童文学与幼儿情感发展的密切联系,即情感教育离不开儿童文学教育、情感熏陶离不开儿童文学渗透、情感发展离不开儿童文学接续.最后,论述了儿童文学促进幼儿情感发展的优化策略,即泛灵原则：开发幼儿潜在天性;游戏原则：叩响幼儿情商之门;互动原则：指引幼儿审美途径.     

图书馆核心员工离职行为对图书馆创新能力影响研究

图书馆核心员工对于图书馆的发展具有关键作用,其离职行为会对图书馆创新能力造成很大负面影响.文章从图书馆核心员工离职这一现象出发,结合管理学中对离职原因的多路径"展开"模型理论及工作嵌入理论,进行了图书馆核心员工界定、其主动离职行为对图书馆创新能力的影响、主动离职行为的原因、以及削弱这种影响的员工保持策略的研究.     

Legal case document similarity: You need both network and text

Estimating the similarity between two legal case documents is an important and challenging problem, having various downstream applications such as prior-case retrieval and citation recommendation. There are two broad approaches for the task — citation network-based and text-based. Prior citation network-based approaches consider citations only to prior-cases (also called precedents) (PCNet). This approach misses important signals inherent in Statutes (written laws of a jurisdiction). In this work, we propose Hier-SPCNet that augments PCNet with a heterogeneous network of Statutes. We incorporate domain knowledge for legal document similarity into Hier-SPCNet, thereby obtaining state-of-the-art results for network-based legal document similarity.Both textual and network similarity provide important signals for legal case similarity; but till now, only trivial attempts have been made to unify the two signals. In this work, we apply several methods for combining textual and network information for estimating legal case similarity. We perform extensive experiments over legal case documents from the Indian judiciary, where the gold standard similarity between document-pairs is judged by law experts from two reputed Law institutes in India. Our experiments establish that our proposed network-based methods significantly improve the correlation with domain experts’ opinion when compared to the existing methods for network-based legal document similarity. Our best-performing combination method (that combines network-based and text-based similarity) improves the correlation with domain experts’ opinion by 11.8% over the best text-based method and 20.6% over the best network-based method. We also establish that our best-performing method can be used to recommend/retrieve citable and similar cases for a source (query) case, which are well appreciated by legal experts.     

Examining the effect of whitening on static and contextualized word embeddings

Static word embeddings (SWE) and contextualized word embeddings (CWE) are the foundation of modern natural language processing. However, these embeddings suffer from spatial bias in the form of anisotropy, which has been demonstrated to reduce their performance. A method to alleviate the anisotropy is the “whitening” transformation. Whitening is a standard method in signal processing and other areas, however, its effect on SWE and CWE is not well understood. In this study, we conduct an experiment to elucidate the effect of whitening on SWE and CWE. The results indicate that whitening predominantly removes the word frequency bias in SWE, and biases other than the word frequency bias in CWE.     

Learning interpretable word embeddings via bidirectional alignment of dimensions with semantic concepts

We propose bidirectional imparting or BiImp, a generalized method for aligning embedding dimensions with concepts during the embedding learning phase. While preserving the semantic structure of the embedding space, BiImp makes dimensions interpretable, which has a critical role in deciphering the black-box behavior of word embeddings. BiImp separately utilizes both directions of a vector space dimension: each direction can be assigned to a different concept. This increases the number of concepts that can be represented in the embedding space. Our experimental results demonstrate the interpretability of BiImp embeddings without making compromises on the semantic task performance. We also use BiImp to reduce gender bias in word embeddings by encoding gender-opposite concepts (e.g., male–female) in a single embedding dimension. These results highlight the potential of BiImp in reducing biases and stereotypes present in word embeddings. Furthermore, task or domain-specific interpretable word embeddings can be obtained by adjusting the corresponding word groups in embedding dimensions according to task or domain. As a result, BiImp offers wide liberty in studying word embeddings without any further effort.     

浅析英文歌曲在中学英语教学中的运用

随着新课改步伐的不断加快,学生学习兴趣的培养成为教学的重中之重。在中学英语课堂教学中,如何更好地培养学生的学习兴趣显得尤为重要。英文歌曲作为中学英语课堂的辅助材料,不仅能达到语言训练的目的,还可以最大程度地调动学生的主观能动性,有利于其潜能的开发,优化教学效果,提高学生的英语水平。本文从英文歌曲在中学英语教学中的重要作用这一方面着手,对英文歌曲在中学英语教学中的运用加以浅析。