汉英句子扩展模式之对比研究及相关汉译英策略

翻译是一个比较差异、解决差异的过程,而句子是交际活动中的基本语言单位。现从句子层面对比分析英汉两种语言不同的句子扩展模式,即汉语的句首开放性与英语的句尾开放性,以及由此导致的不同句式特点,并探讨相关的汉译英策略。     

高职扩招背景下三类重点群体的人才培养模式探析——以财经专业群为例

教育部《高职扩招专项工作实施方案》的发布,标志着高职院校扩招100万进入实施阶段。此次扩招的对象主要是退役军人、下岗工人以及农民工三个重点群体。新型招生群体的出现要求各高职院校亟需制定学制和授课形式灵活、可操作性强、符合地方经济和产业发展水平的人才培养模式。财经类专业群与理工类、农学类、医学类等专业群相比有其独特优势,更加适合上述群体,在人才培养中可采用灵活的学制、授课形式及置换课程方式。     

能力集扩展的表格方法

每个具有非对称权重的有向图均可用一个称为“扩展表”的矩阵或表格来表示 .讨论了扩展表中的“圈”和“生成表”的概念及其基本特性 ,给出了一种寻找有向图最小生成树的表格方法——最小生成表法 .研究了最小生成表算法在最优能力集扩展问题中的应用 ,给出了一个算法的具体示例 ,并分析了有关的需研究的问题和可能的拓展     

基于零点的逻辑函数bj图和dj图的转换

图形化简法是逻辑函数化简的有效工具。bi图和西图分别是RM展开式、CRM展开式的图珏表示。通过对RM展开式、CRM展开式系数关系的分析，提出了2种bj图和dj图的图形转换方法，并廷各种方法予以实例说明。对于5变量以下的逻辑函数，这二种方法具有简单、直观等特点。     

Optimization of some factors affecting the performance of query expansion

This paper examines the factors affecting the performance of global query expansion based on term co-occurrence data and suggests a way to maximize the retrieval effectiveness. Major parameters to be optimized through experiments are term similarity measure and the weighting scheme of additional terms. The evaluation of four similarity measures tested in query expansion reveal that mutual information and Yule's Y, which emphasize low frequency terms, achieve better performance than cosine and Jaccard coefficients that have the reverse tendency. In the evaluation of three weighting schemes, similarity weight performs well only with short queries, whereas fixed weights of approximately 0.5 and similarity rank weights were effective with queries of any length. Furthermore, the optimal similarity rank weight achieving the best overall performance seems to be the least affected by test collections and the number of additional terms. For the efficiency of retrieval, the number of additional terms needs not exceed 70 in our test collections, but the optimal number may vary according to the characteristics of the similarity measure employed.     

基于代数分解的MSI组合电路扩展技术

讨论了一位全加器及优编器的逻辑功能，给出了适当的代数表示形式，在此基础上提出了基于代数分解方法的扩展技术。     

Parsimonious translation models for information retrieval

In the KL divergence framework, the extended language modeling approach has a critical problem of estimating a query model, which is the probabilistic model that encodes the user’s information need. For query expansion in initial retrieval, the translation model had been proposed to involve term co-occurrence statistics. However, the translation model was difficult to apply, because the term co-occurrence statistics must be constructed in the offline time. Especially in a large collection, constructing such a large matrix of term co-occurrences statistics prohibitively increases time and space complexity. In addition, reliable retrieval performance cannot be guaranteed because the translation model may comprise noisy non-topical terms in documents. To resolve these problems, this paper investigates an effective method to construct co-occurrence statistics and eliminate noisy terms by employing a parsimonious translation model. The parsimonious translation model is a compact version of a translation model that can reduce the number of terms containing non-zero probabilities by eliminating non-topical terms in documents. Through experimentation on seven different test collections, we show that the query model estimated from the parsimonious translation model significantly outperforms not only the baseline language modeling, but also the non-parsimonious models.     

A review of ontology based query expansion

This paper examines the meaning of context in relation to ontology based query expansion and contains a review of query expansion approaches. The various query expansion approaches include relevance feedback, corpus dependent knowledge models and corpus independent knowledge models. Case studies detailing query expansion using domain-specific and domain-independent ontologies are also included. The penultimate section attempts to synthesise the information obtained from the review and provide success factors in using an ontology for query expansion. Finally the area of further research in applying context from an ontology to query expansion within a newswire domain is described.     

An empirical study of query expansion and cluster-based retrieval in language modeling approach

The term mismatch problem in information retrieval is a critical problem, and several techniques have been developed, such as query expansion, cluster-based retrieval and dimensionality reduction to resolve this issue. Of these techniques, this paper performs an empirical study on query expansion and cluster-based retrieval. We examine the effect of using parsimony in query expansion and the effect of clustering algorithms in cluster-based retrieval. In addition, query expansion and cluster-based retrieval are compared, and their combinations are evaluated in terms of retrieval performance by performing experimentations on seven test collections of NTCIR and TREC.