一种中文网页自动分类方法的实现及应用

为了有效地组织和分析海量的Web信息，文章应用有指导的机器学习方法实现了一个中文网页分类器，并应用该分类器在“天网”搜索引擎上实现了大规模中文网页的目录导航服务。实验结果表明该分类器有较高的分类质量，满足了中文网页自动分类的要求。     

结合类频率的关联中文文本分类

该文提出一种词类频率和关联中文文本分类相结合的算法ARCTC。此算法将文档视作事务,关键词视作项,并针对文本事务的特性,提出利用词的类频率筛选与分类相关性不大的词汇,然后将改进的关联规则挖掘算法用于挖掘项和类别间的相关关系。挖掘出的规则用于形成类别特征词的集合,可用来和类标号未知文档的词的集合求交集,交集元素个数最多者即为所分类别。实验证明,该算法在提高训练时间和测试时间的同时具有较好的召回率、准确率和F-Measure。     

Category Learning in Rhesus Monkeys: A Study of the Shepard, Hovland, and Jenkins (1961) Tasks.

In influential research, R. N. Shepard, C. I. Hovland, and H. M. Jenkins (1961) surveyed humans' categorization abilities using tasks based in rules, exclusive-or (XOR) relations, and exemplar memorization. Humans' performance was poorly predicted by cue-conditioning or stimulus-generalization theories, causing Shepard et al. to describe it in terms of hypothesis selection and rule application that were possibly supported by verbal mediation. The authors of the current article surveyed monkeys' categorization abilities similarly. Monkeys, like humans, found category tasks with a single relevant dimension the easiest and perceptually chaotic tasks requiring exemplar memorization the most difficult. Monkeys, unlike humans, found tasks based in XOR relations very difficult. The authors discuss the character and basis of the species difference in categorization and consider whether monkeys are the generalization-based cognitive system that humans are not. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

一个基于字特征的文本分类模型

提出了一种基于字特征的中文文本分类方法。该方法的出发点是变常用的基于表层的匹配为基于概念的匹配,用汉字特征向量作为文本的表示方法。算法根据文本中汉字的特征建立文本表示矩阵和类别表示矩阵,并通过线性最小二乘算法形成分类矩阵。     

An Evaluation of Passage-Based Text Categorization

Researches in text categorization have been confined to whole-document-level classification, probably due to lack of full-text test collections. However, full-length documents available today in large quantities pose renewed interests in text classification. A document is usually written in an organized structure to present its main topic(s). This structure can be expressed as a sequence of subtopic text blocks, or passages. In order to reflect the subtopic structure of a document, we propose a new passage-level or passage-based text categorization model, which segments a test document into several passages, assigns categories to each passage, and merges the passage categories to the document categories. Compared with traditional document-level categorization, two additional steps, passage splitting and category merging, are required in this model. Using four subsets of the Reuters text categorization test collection and a full-text test collection of which documents are varying from tens of kilobytes to hundreds, we evaluate the proposed model, especially the effectiveness of various passage types and the importance of passage location in category merging. Our results show simple windows are best for all test collections tested in these experiments. We also found that passages have different degrees of contribution to the main topic(s), depending on their location in the test document.     

Category-specific scene categorization

Scene categorization with category-specific visual-word construction and image representation is proposed in this study. The proposed scene categorization has effective discriminative ability and class extendibility. The reasons are listed as follows. First, since the visual-word construction and image representation are category-specific, the corresponding learning model for classification has substantial discriminating power. Second, since the visual-word construction and image representation are category-specific, image features related to the original classes need not be recreated when new classes are added, which minimizes reconstruction overhead. Experimental results confirm that the accuracy of the proposed method is superior to existing methods with single-type features both in single-scale and in multi-scale versions.     

Hierarchical behavior categorization using correlation based adaptive resonance theory

This paper introduces a new model for robot behavior categorization. Correlation based adaptive resonance theory (CobART) networks are integrated hierarchically in order to develop an adequate categorization, and to elicit various behaviors performed by the robot. The proposed model is developed by adding a second layer CobART network which receives first layer CobART network categories as an input, and back-propagates the matching information to the first layer networks. The first layer CobART networks categorize self-behavior data of a robot or an object in the environment while the second layer CobART network categorizes the robot's behavior with respect to its effect on the object. Experiments show that the proposed model generates reasonable categorization of behaviors being tested. Moreover, it can learn different forms of the behaviors, and it can detect the relations between them. In essence, the model has an expandable architecture and it contains reusable parts. The first layer CobART networks can be integrated with other CobART networks for another categorization task. Hence, the model presents a way to reveal all behaviors performed by the robot at the same time.     

The emotional quality of speech in voice services

The emotional quality of speech is defined as the global qualitative and hedonic impressions experienced by listeners. This research investigated the emotional quality of speech samples used in voice services. In a first experiment listening tests were conducted using 200 messages generated by 20 female speakers who pronounced two sentences in five elocution styles. Listeners grouped the messages according to similarities in terms of the impression of the messages. Verbal comments regarding hedonic effect on the listener and acoustic parameters of the voices' timbre and intonation were analysed. In a second experiment, the 200 messages were evaluated according to 20 criteria extracted from the first experiment. The results produced a precise perceptive portrait for each sequence, giving a full picture of the listeners' impressions of what they heard. The results can be applied to the design of voice services, as was done for the voicemail of France Telecom Orange.     

基于原型范畴理论的大学英语词汇教学研究

范畴化是一种抽象的认知活动,是概念和语义形成的出发点。它基于身体体验,随着认识的发展和特殊的需要不断形成新范畴,扩展老范畴。因此,对于范畴的界定应该是动态的。在英语词汇学习中,可以通过形成词汇链、建立多义词语义网络和积累下义层次词这些静态手段,培养学生的范畴化能力。学生还可以根据自己的学习任务,通过各种手段自主地进行范畴化,使符合任务要求的词汇进入到一个临时建构的范畴中。通过词汇的范畴化和重新范畴化过程,充分发挥学生的能动性,提高学生的学习效率和英语综合能力。