试谈翻译中理解的误区

理解原作是一切翻译活动不可或缺的环节,是正确表达的基础和前提,也是一项很艰巨的工作.本文以<棋王>英译文例,就其在多义词、句子结构、文化词语三方面对原作一些句子的理解提出质疑,并给出参考译文.     

"有没有VP"与"VP没有"的比较研究

一种新句式叙述性“有没有VP”的出现,势必对普通话句式的格局产生一定的影响。它与普通话传统用语“VP没有”都是选择问句,表达的基本意义相近,使用时,人们往往把这两种形式看成是完全等同的。本文着眼于这两种句式的“微殊”,试从形成模式、问话期待、语义重心和回答方式、句式特点和变换方式上作一些探讨,以期更好地指导人们的言语实践。     

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.     

Building parallel corpora by automatic title alignment using length-based and text-based approaches

Cross-lingual semantic interoperability has drawn significant attention in recent digital library and World Wide Web research as the information in languages other than English has grown exponentially. Cross-lingual information retrieval (CLIR) across different European languages, such as English, Spanish, and French, has been widely explored; however, CLIR across European languages and Oriental languages is still in the initial stage. To cross language boundary, corpus-based approach is promising to overcome the limitation of the knowledge-based and controlled vocabulary approaches but collecting parallel corpora between European language and Oriental language is not an easy task. Length-based and text-based approaches are two major approaches to align parallel documents. In this paper, we investigate several techniques using these approaches and compare their performances in aligning English and Chinese titles of parallel documents available on the Web.     

基于Vague相似度量理论的园林喷灌设计方案评价方法

在描述Vague之间的相似度量与加权相似度量方法的基础上,提出了方案评价所采用的评价指标体系,形成了一套喷灌设计方案评价新方法.在园林喷灌设计方案评价中,Vague集之间的相似度量是评价设计方案接近期望方案（理想方案）的度量,相似度量值越大,设计方案接近期望方案越好.通过实例阐明Vague集之间的相似度量在喷灌设计方案评价中的可行性,方案评价取得了较为满意的结果.     

基于本体的粒度计算模型

本文描述了基于本体的粒度计算模型。利用Ontology技术,给出了概念的形式化表示,并构造出一种新的粒度计算模型以及该模型的相关定义。     

基于句子匹配的文章自写度测评系统

针对人工进行不同文章中相同内容的判断存在着较大困难的局面，提出一个基于句子匹配的文章自写度测评系统。设计基于句子匹配的文章自写度测评系统的系统结构，论述句子分析器、句子匹配器与文章自写度评价器3个关键模块的详细流程，并设计相应算法。选取小规模数据进行实验，实验结果表明，基于句子匹配的文章自写度测评系统从技术上完全可行。最后分析基于句子匹配的文章自写度测评系统的难点及问题。     

基于句子相似度的文档复制检测算法研究

提出一种基于句子相似度的文档复制检测技术，在抓住文档的全局特征的同时又兼顾文档的结构信息，克服以往检测算法两者不可兼顾的缺陷，提高检测精度。最后，给出该算法与其他算法检测结果的比较情况。实验证明，该算法是可行的。     

Text summarization using Wikipedia

Automatic text summarization has been an active field of research for many years. Several approaches have been proposed, ranging from simple position and word-frequency methods, to learning and graph based algorithms. The advent of human-generated knowledge bases like Wikipedia offer a further possibility in text summarization – they can be used to understand the input text in terms of salient concepts from the knowledge base. In this paper, we study a novel approach that leverages Wikipedia in conjunction with graph-based ranking. Our approach is to first construct a bipartite sentence–concept graph, and then rank the input sentences using iterative updates on this graph. We consider several models for the bipartite graph, and derive convergence properties under each model. Then, we take up personalized and query-focused summarization, where the sentence ranks additionally depend on user interests and queries, respectively. Finally, we present a Wikipedia-based multi-document summarization algorithm. An important feature of the proposed algorithms is that they enable real-time incremental summarization – users can first view an initial summary, and then request additional content if interested. We evaluate the performance of our proposed summarizer using the ROUGE metric, and the results show that leveraging Wikipedia can significantly improve summary quality. We also present results from a user study, which suggests that using incremental summarization can help in better understanding news articles.