Extending WHIRL with background knowledge for improved text classification

Intelligent use of the many diverse forms of data available on the Internet requires new tools for managing and manipulating heterogeneous forms of information. This paper uses WHIRL, an extension of relational databases that can manipulate textual data using statistical similarity measures developed by the information retrieval community. We show that although WHIRL is designed for more general similarity-based reasoning tasks, it is competitive with mature systems designed explicitly for inductive classification. In particular, WHIRL is well suited for combining different sources of knowledge in the classification process. We show on a diverse set of tasks that the use of appropriate sets of unlabeled background knowledge often decreases error rates, particularly if the number of examples or the size of the strings in the training set is small. This is especially useful when labeling text is a labor-intensive job and when there is a large amount of information available about a particular problem on the World Wide Web.

The Twelve Cranial Nerves of Christmas: Mnemonics,Rhyme, and Anatomy – Seeing the Lighter Side

Anatomy, has in history, been linked to helpful ways to remember structures, branches of nerves, structures passing through foramina, etc. Scalp is even a mnemonic in itself (S kin, C onnective tissue, A poneurosis, L oose areolar tissue, P ericranium). There has been concern by some educators that using mnemonics or rhymes promotes a surface approach to learning and is unhelpful in establishing long-term and meaningful deep learning. This article argues that mnemonics and rhyme can be used, in the appropriate way, at the right time, by students as an important learning strategy. That strategy can help lay a foundation of knowledge to be developed and later built upon, or simply recall information more easily. Mnemonics, like all information that is to be recalled, is consolidated by rehearsal. In examining the neuroanatomy of learning theories, it is therefore possible to suggest that when students begin to learn an area of anatomy, such as the cranial nerves, using a mnemonic or rhyme, it can help students remember the names and facilitate the engagement of the working memory processes assisting the student to build a construct for subsequent deeper layers of knowledge. Modern approaches to anatomy education involve a myriad of learning opportunities, but educators must assess the value of each one before recommending them to students. It appears that using mnemonics and rhyme is as valid today as it has been for centuries.     

基于小波和生物视觉机制的感兴趣区域提取方法

提出一种基于小波并结合生物视觉机制的感兴趣区域提取算法,首先使用小波构建亮度、颜色和方向等3个特征的小波金字塔,然后使用中央—周边算法得到每个特征的关注图,对关注图进行迭代运算以进一步抑制不显著区域。对迭代处理后的显著图进行线性组合得到最终的显著图;使用WTA网络和返回抑制机制提取显著点,以显著点为中心进行区域生长得到最终的显著区域。实验证明,本方法在提取效果上较原方法提高了4.2%和效率上较原方法提高了28.75%,总体上较原方法有了明显改善。     

网络环境下的个性化检索定制服务

在信息爆炸的网络环境下,信息获取效率和准确度成为用户关注的重点,同时又由于用户的个性化需求在进行检索时变得越来越明显,因此,迫切需要数字图书馆能够由被动服务转变为主动服务,为用户提供更加人性化的服务.文章就此论述了个性化检索定制服务的模式和实现方案.     

信息检索技术及其在图书馆信息服务中的应用研究

对图书馆利用检索技术开展联机公共书目查询、信息导航及科技查新等方面进行了探讨,提出了信息检索服务发展的方向:集成化、个性化、标准化。     

应用性本科信息检索课教学改革的构想

应用性本科院校培养的是技术型、实用型人才。因而应用性本科院校信息检索课的教学内容和课程重点，应协调好理论与实战、检索与利用、中文信息与外文信息的关系。增加信息服务系统介绍、加强阅读指导、改革教材等几个方面进行改革。     

基于语义矩阵的图像检索相关反馈技术

针对基于语义网络的图像检索中的过反馈问题,本文提出了将语义网络转换为语义矩阵,采用基于多用户相关反馈的投票机制,可有效地克服过反馈.在此基础上提出了一种新颖的新图添加策略和处理语义向量查询的有效方法,最后指出了还需进一步研究的问题.     

试论搜索引擎的技术改进

作为当前网络信息资源的重要检索工具 ,搜索引擎问世以来已得到广泛使用。作为一个新生事物 ,搜索引擎技术得到了快速发展 ,但同时也在广大网络用户的频繁使用中 ,逐渐暴露出其存在的各种各样的问题。本文主要从 3个不同方面探讨对搜索引擎进行技术改进的方法与思路。1　搜索引擎工作模式的改变众所周知 ,Ｗｅｂ最初是作为欧洲粒子物理研究中心(ＣＥＲＮ)内部的协作环境而诞生的 ,其分布式协作特性已成为全球信息共享的基础。但值得注意的是 ,基于Ｗｅｂ的一些信息应用系统采用的却是传统的集中式处理模式 ,以致这种集中式处理与Ｗｅｂ的分…     

万维网信息智能检索及其在语音网站中的应用

A scheme for designing a voice Website-based intelligent WWW information retrieval system is presented.It provides convenient and rapid information retrieval service for more users.Combining the advantages of full-text retrieval and intelligent retrieval,it can improve the response speed,recall ratio and pertinency ratio.Meanwhile,by using ASR,TIS and natural language processing,it can enlarge the scale of Internet users.     

Searching strategies for the Hungarian language

This paper reports on the underlying IR problems encountered when dealing with the complex morphology and compound constructions found in the Hungarian language. It describes evaluations carried out on two general stemming strategies for this language, and also demonstrates that a light stemming approach could be quite effective. Based on searches done on the CLEF test collection, we find that a more aggressive suffix-stripping approach may produce better MAP. When compared to an IR scheme without stemming or one based on only a light stemmer, we find the differences to be statistically significant. When compared with probabilistic, vector-space and language models, we find that the Okapi model results in the best retrieval effectiveness. The resulting MAP is found to be about 35% better than the classical tf idf approach, particularly for very short requests. Finally, we demonstrate that applying an automatic decompounding procedure for both queries and documents significantly improves IR performance (+10%), compared to word-based indexing strategies.