英汉姓名异同及其所蕴涵的文化底蕴

本文试从英汉两种不同语言所表达的姓名的共同性与差异性角度出发 ,用对比分析的方法去探求与领略英语和汉语两种语言的姓名所蕴涵的深远文化底蕴 ,窥探广博的语言文化的风采     

语义系统中的词项空缺与交际中的可行补偿手段

从语义学的角度,对一定语义场中的部分词汇,特别是带有“文化负荷”的词汇的词义进行义素分析,对聚合分析中出现的“词项空缺”进行义素推导,对其在交际过程中造成的不便提供一定的补偿手段,以期对英语的词汇教学起到有一定的启发作用。     

笛卡尔和培根认识论共性初探

哲学界把培根和笛卡尔称为经验论和唯理论的始祖,其实他们对经验与理性,归纳与演绎关系坚持了辩证处理,狭隘经验论和纯粹唯理论是其以后的发展深化所导致的。只不过在培根和笛卡尔那里孕育着分裂的种子而已。     

隐喻的认知基础与中英隐喻的相似性

根据有关隐喻的一种最新理论,隐喻不能单纯作为语言内部的一种修辞手段,而应把它和人类的认知过程相联系,隐喻能反映语言和人类思维、认知的关系.由于隐喻有这种共同的认知基础,不同语言文化中的隐喻自然表现出一定的相似性.     

数字图书馆中三维模型检索技术研究

三维数据模型正在成为数字图书馆中的重要信息,三维模型检索则是必须要解决的技术关键。本文较系统地介绍了该研究方向的现状,分析了其中的数据获取、特征提取、特征空间中的相似度量、相关反馈等关键技术,并提出了今后研究的方向。     

基于语义搭配的汉语短语界定研究

简单分析了语法上界定汉语短语的困扰,提出一种利用语义搭配关系界定汉语短语的方法。首先,借助同义词词林实现语义知识的编码,用这种语义编码来表示语义搭配关系;其次,在此基础上,定义短语与已知语义搭配关系的相似性,计算词语搭配的合理性;最后,利用短语内部的语义搭配合理性优于其他搭配这一性质,用算法实现了基于语义的汉语短语界定过程。该方法应用于军事文本,从中界定出描述作战单位等信息的短语,取得较好的效果。此外,经该方法界定出的短语具有较强的语义信息,对信息抽取等实际应用具有一定的适用性。     

基于词向量模型的中文序列比对研究

[目的/意义]针对生物信息学中著名的序列比对算法在文本相似度中的应用,改进前人的方法并提高文本相似度计算的准确性。[方法/过程]首先,对目标文本进行规范化处理,构成中文序列集。随后,利用训练好的Word2vec中的Skip-Gram模型来构建该中文序列集的语词对打分矩阵并制定好打分规则。最后,对中文序列两两进行全局比对并获得比对的最优解,回溯得到最优解的比对路径,计算中文序列的相似度。[结果/结论]实证结果表明,相较于传统方法,本文方法融合词向量模型提升文本相似度计算的准确性并有效解决传统方法中出现重复词对的问题。     

Assessing kindergarteners’ mathematics problem solving: The development of a cognitive diagnostic test

This study aimed to develop an instrument for assessing kindergarteners’ mathematics problem solving (MPS) by using cognitive diagnostic assessment (CDA). A total of 747 children were recruited to examine the psychometric properties of the cognitive diagnostic test. The results showed that the classification accuracy of 11 cognitive attributes ranged from .68 to .99, with the average being .84. Both the cognitive diagnostic test score and the average mastery probabilities of the 11 cognitive attributes had moderate correlations with the Applied Problem subtest and the Calculation subtest of the Woodcock–Johnson IV Tests of Achievement. Moreover, the correlation between the cognitive diagnostic test and the Applied Problems subtest was higher than that between the cognitive diagnostic test and the Calculation subtest. The results indicated that the formal cognitive diagnostic test was a reliable instrument for assessing kindergarteners’ MPS in the domain of number and operations.     

Semantic text classification: A survey of past and recent advances

Automatic text classification is the task of organizing documents into pre-determined classes, generally using machine learning algorithms. Generally speaking, it is one of the most important methods to organize and make use of the gigantic amounts of information that exist in unstructured textual format. Text classification is a widely studied research area of language processing and text mining. In traditional text classification, a document is represented as a bag of words where the words in other words terms are cut from their finer context i.e. their location in a sentence or in a document. Only the broader context of document is used with some type of term frequency information in the vector space. Consequently, semantics of words that can be inferred from the finer context of its location in a sentence and its relations with neighboring words are usually ignored. However, meaning of words, semantic connections between words, documents and even classes are obviously important since methods that capture semantics generally reach better classification performances. Several surveys have been published to analyze diverse approaches for the traditional text classification methods. Most of these surveys cover application of different semantic term relatedness methods in text classification up to a certain degree. However, they do not specifically target semantic text classification algorithms and their advantages over the traditional text classification. In order to fill this gap, we undertake a comprehensive discussion of semantic text classification vs. traditional text classification. This survey explores the past and recent advancements in semantic text classification and attempts to organize existing approaches under five fundamental categories; domain knowledge-based approaches, corpus-based approaches, deep learning based approaches, word/character sequence enhanced approaches and linguistic enriched approaches. Furthermore, this survey highlights the advantages of semantic text classification algorithms over the traditional text classification algorithms.     

Discovering socially similar users in social media datasets based on their socially important locations

Socially similar social media users can be defined as users whose frequently visited locations in their social media histories are similar. Discovering socially similar social media users is important for several applications, such as, community detection, friendship analysis, location recommendation, urban planning, and anomaly user and behavior detection. Discovering socially similar users is challenging due to dataset size and dimensions, spam behaviors of social media users, spatial and temporal aspects of social media datasets, and location sparseness in social media datasets. In the literature, several studies are conducted to discover similar social media users out of social media datasets using spatial and temporal information. However, most of these studies rely on trajectory pattern mining methods or take into account semantic information of social media datasets. Limited number of studies focus on discovering similar users based on their social media location histories. In this study, to discover socially similar users, frequently visited or socially important locations of social media users are taken into account instead of all locations that users visited. A new interest measure, which is based on Levenshtein distance, was proposed to quantify user similarity based on their socially important locations and two algorithms were developed using the proposed method and interest measure. The algorithms were experimentally evaluated on a real-life Twitter dataset. The results show that the proposed algorithms could successfully discover similar social media users based on their socially important locations.