基于翻译检索一体化的跨语言信息检索研究

从检索方法、结果展现和评估方法三方面分析跨语言信息检索的现状,引出翻译检索一体化的跨语言信息检索方法,把翻译、检索结合在同一检索模型中.一体化将翻译和检索中的不确定性组合在一起,从而做出更全局的优化,减少跨语言信息检索与单语检索之间的差距,从而充分利用单语检索中的有用方法;并针对万方数据现有资源,构建出面向科技文献的跨语言信息检索系统框架.     

基于科技文献资源的跨语言信息检索系统研究

本文分析了跨语言信息检索的现状,介绍和描述跨语言信息检索的主要问题,并针对万方数据和NSTL的现有资源,构建出基于科技文献的跨语言信息检索原型系统,此原型系统通过字典查询得到检索词翻译,应用Google在线翻译进行标题、摘要翻译,并预留中国科学院自动化所提供的翻译引擎扩展.跨语言信息检索强调检索结果翻译的可读性,帮助用户理解基本大意,不刻意追求准确性,力求在应用过程中促进机器翻译技术的进步和发展,辅助科研.     

跨语言信息检索可视化研究

语言的多样性限制了人们利用信息的自由，也影响了信息价值的充分发挥。因此，跨语言信息检索成为当今的研究热点。本文将信息可视化的相关方法和技术应用到跨语言信息检索领域当中，提出了跨语言信息检索可视化模型，并介绍了一个实例——澳门法律信息可视化系统。     

跨语言信息检索的知识图谱研究

在ISI的WebofScience中以“跨语言信息检索”为主题进行检索,利用共引分析、共词分析、信息可视化等方法和技术,借助CiteSpace1I信息可视化软件绘制跨语言信息检索研究领域的文献共引网络图谱、热点词汇图谱以及前沿词汇图谱,直观地展示和分析不同时期跨语言信息检索领域的代表人物及代表文献,揭示其研究热点,探测其研究前沿及发展趋势。     

跨语言信息检索技术应用与进展研究

本文介绍了跨语言信息检索技术的基本方法和相关的重要国际会议，并对跨语言信息检索的研究热点难点、应用概况、最新的中文的跨语言信息检索系统的研究情况进行了分析，最后提出其发展趋势和应用前景。     

信息检索模型及其在跨语言信息检索中的应用进展

信息检索发展中的一个重要理论问题是如何对查询与文档进行匹配,由此形成了不同的信息检索模型。跨语言信息检索是信息检索研究的一个分支,也是近年来的热点问题。本文主要对信息检索模型的研究进展,及其在跨语言信息检索中的应用进展进行分析与综述。     

基于本体的跨语言信息检索在数字图书馆中的应用

首先对跨语言信息检索和相关技术进行了介绍,了解当前跨语言信息检索技术的不足,然后阐述了传统跨语言信息检索技术在数字图书馆应用中的局限性,并由此引出了基于本体的跨语言技术。最后提出了一种基于本体的数字图书馆跨语言信息检索系统,并详细阐述了系统的流程,着重讲述了数字图书馆跨语言领域本体的构建。由于本体具有良好的概念层次和对逻辑推理的支持,对源语言和目标语言进行语义扩展,提高了数字图书馆跨语言系统的检索效率。     

跨语言信息检索技术与应用研究

介绍了跨语言信息检索理论的提出及发展，简介了相关的国际会议；阐述了跨语言信息检索的主要技术方法，并对跨语言信息检索理论应用研究进行了探讨，展望了跨语言信息检索的发展前景。     

跨语言信息检索初探

随着互联网的发展，网络成为人们获取信息的重要来源，但是语言的藩篱却限制了人们获取信息的自由。跨语言信息检索应运而生。本文将从理论和实践两方面对跨语言信息检索进行论述：理论方面分析了实现跨语言信息检索的方法和策略；实践方面则以Mulinex系统和Google搜索引擎为例，介绍了跨语言信息检索在实际中的应用。     

近年来我国跨语言信息检索研究的统计分析

以CNKI数字出版平台所收录的文献为依据,对近年来我国跨语言信息检索(CLIR:Cross Language Information Retrieval)的文献进行文献计量学统计,选取2001-2012年间研究成果作为数据样本,并从文献年代分布、文献被引情况、文献情报源分布、研究人员及机构分布、获得资助情况、关键词及论文主题分布进行统计分析,对2001-2012年关于跨语言信息检索的研究现状进行了梳理和总结,从而为进一步的研究和发展提供参考。     

查询翻译方法研究——以汉英跨语言信息检索为例

【目的/意义】跨语言信息检索研究的目的即在消除因语言的差异而导致信息查询的困难,提高从大量纷繁 复杂的查找特定信息的效率。同时提供一种更加方便的途径使得用户能够使用自己熟悉的语言检索另外一种语 言文档。【方法/过程】本文通过对国内外跨语言信息检索的研究现状分析,介绍了目前几种查询翻译的方法,包括： 直接查询翻译、文献翻译、中间语言翻译以及查询—文献翻译方法,对其效果进行比较,然后阐述了跨语言检索关 键技术,对使用基于双语词典、语料库、机器翻译技术等产生的歧义性提出了解决方法及评价。【结果/结论】使用自 然语言处理技术、共现技术、相关反馈技术、扩展技术、双向翻译技术以及基于本体信息检索技术确保知识词典的 覆盖度和歧义性处理,通过对跨语言检索实验分析证明采用知识词典、语料库和搜索引擎组合能够提高查询效 率。【创新/局限】本文为了解决跨语言信息检索使用词典、语料库中词语缺乏的现象,提出通过搜索引擎从网页获 取信息资源来充实语料库中语句对不足的问题。文章主要针对中英文信息检索问题进行了探讨,解决方法还需要 进一步研究,如中文切词困难以及字典覆盖率低等严重影响检索的效率。     

NTCIR-2 as a Rosetta stone in laboratory experiments of IR systems

This paper presents a laboratory based evaluation study of cross-language information retrieval technologies, utilizing partially parallel test collections, NTCIR-2 (used together with NTCIR-1), where Japanese–English parallel document collections, parallel topic sets and their relevance judgments are available. These enable us to observe and compare monolingual retrieval processes in two languages as well as retrieval across languages. Our experiments focused on (1) the Rosetta stone question (whether a partially parallel collection helps in cross-language information access or not?) and (2) two aspects of retrieval difficulties namely “collection discrepancy” and “query discrepancy”. Japanese and English monolingual retrieval systems are combined by dictionary based query translation modules so that a symmetrical bilingual evaluation environment is implemented.     

Technical issues of cross-language information retrieval: a review

This paper reviews state-of-the-art techniques and methods for enhancing effectiveness of cross-language information retrieval (CLIR). The following research issues are covered: (1) matching strategies and translation techniques, (2) methods for solving the problem of translation ambiguity, (3) formal models for CLIR such as application of the language model, (4) the pivot language approach, (5) methods for searching multilingual document collection, (6) techniques for combining multiple language resources, etc.     

Matching meaning for cross-language information retrieval

This article describes a framework for cross-language information retrieval that efficiently leverages statistical estimation of translation probabilities. The framework provides a unified perspective into which some earlier work on techniques for cross-language information retrieval based on translation probabilities can be cast. Modeling synonymy and filtering translation probabilities using bidirectional evidence are shown to yield a balance between retrieval effectiveness and query-time (or indexing-time) efficiency that seems well suited large-scale applications. Evaluations with six test collections show consistent improvements over strong baselines.     

Candidate document retrieval for cross-lingual plagiarism detection using two-level proximity information

The rapid growth of documents in different languages, the increased accessibility of electronic documents, and the availability of translation tools have caused cross-lingual plagiarism detection research area to receive increasing attention in recent years. The task of cross-language plagiarism detection entails two main steps: candidate retrieval and assessing pairwise document similarity. In this paper we examine candidate retrieval, where the goal is to find potential source documents of a suspicious text. Our proposed method for cross-language plagiarism detection is a keyword-focused approach. Since plagiarism usually happens in parts of the text, there is a requirement to segment the texts into fragments to detect local similarity. Therefore we propose a topic-based segmentation algorithm to convert the suspicious document to a set of related passages. After that, we use a proximity-based model to retrieve documents with the best matching passages. Experiments show promising results for this important phase of cross-language plagiarism detection.     

Noun phrases as building blocks for cross-language Search Assistance

This paper presents a Foreign-Language Search Assistant that uses noun phrases as fundamental units for document translation and query formulation, translation and refinement. The system (a) supports the foreign-language document selection task providing a cross-language indicative summary based on noun phrase translations, and (b) supports query formulation and refinement using the information displayed in the cross-language document summaries. Our results challenge two implicit assumptions in most of cross-language Information Retrieval research: first, that once documents in the target language are found, Machine Translation is the optimal way of informing the user about their contents; and second, that in an interactive setting the optimal way of formulating and refining the query is helping the user to choose appropriate translations for the query terms.     

跨语言信息检索查询翻译词典自动构建研究

双语机读词典是基于查询翻译的跨语言信息检索中的常用资源,但是传统的手工构建词典的方法费时费力,本文利用统计方法从英汉句对齐平行语料库中自动获取翻译词典,以用于查询翻译过程中。     

Advanced learning algorithms for cross-language patent retrieval and classification

We study several machine learning algorithms for cross-language patent retrieval and classification. In comparison with most of other studies involving machine learning for cross-language information retrieval, which basically used learning techniques for monolingual sub-tasks, our learning algorithms exploit the bilingual training documents and learn a semantic representation from them. We study Japanese–English cross-language patent retrieval using Kernel Canonical Correlation Analysis (KCCA), a method of correlating linear relationships between two variables in kernel defined feature spaces. The results are quite encouraging and are significantly better than those obtained by other state of the art methods. We also investigate learning algorithms for cross-language document classification. The learning algorithm are based on KCCA and Support Vector Machines (SVM). In particular, we study two ways of combining the KCCA and SVM and found that one particular combination called SVM_2k achieved better results than other learning algorithms for either bilingual or monolingual test documents.     

数字资源整合及检索初探

本文介绍了目前整合数字资源的方式以及相关标准、协议和接口技术,讨论了数字资源整合系统的信息检索原理及跨语言翻译、学科专业分类表、检索语言转换等问题,分析了数字资源整合研究将面临的挑战。