Differences in color naming and color salience in Vietnamese and English

The accepted model of color naming postulates that 11 “basic” color terms representing 11 common perceptual experiences show increased processing salience due to a theorized linkage between perception, visual neurophysiology, and cognition. We tested this theory, originally proposed by Berlin and Kay in 1969. Experiment 1 tested salience by comparing unconstrained color naming across two languages, English and Vietnamese. Results were compared with previous research by Berlin and Kay, Boynton and Olson, and colleagues. Experiment 2 validated our stimuli by comparing OSA, Munsell, and newly rendered “basic” exemplars using colorimetry and behavioral measures. Our results show that the relationship between the visual and verbal domains is more complex than current theory acknowledges. An interpoint distance model of color‐naming behavior is proposed as an alternative perspective on color‐naming universality and color‐category structure. © 2003 Wiley Periodicals, Inc. Col Res Appl, 28, 113–138, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.10131     

Class normalization in centroid-based text categorization

Centroid-based categorization is one of the most popular algorithms in text classification. In this approach, normalization is an important factor to improve performance of a centroid-based classifier when documents in text collection have quite different sizes and/or the numbers of documents in classes are unbalanced. In the past, most researchers applied document normalization, e.g., document-length normalization, while some consider a simple kind of class normalization, so-called class-length normalization, to solve the unbalancedness problem. However, there is no intensive work that clarifies how these normalizations affect classification performance and whether there are any other useful normalizations. The purpose of this paper is three folds; (1) to investigate the effectiveness of document- and class-length normalizations on several data sets, (2) to evaluate a number of commonly used normalization functions and (3) to introduce a new type of class normalization, called term-length normalization, which exploits term distribution among documents in the class. The experimental results show that a classifier with weight-merge-normalize approach (class-length normalization) performs better than one with weight-normalize-merge approach (document-length normalization) for the data sets with unbalanced numbers of documents in classes, and is quite competitive for those with balanced numbers of documents. For normalization functions, the normalization based on term weighting performs better than the others on average. For term-length normalization, it is useful for improving classification accuracy. The combination of term- and class-length normalizations outperforms pure class-length normalization and pure term-length normalization as well as unnormalization with the gaps of 4.29%, 11.50%, 30.09%, respectively.     

It's a sign of the kind: Gestures and words guide infants' inductive inferences.

The role of words and gestures in guiding infants' inductive inferences about nonobvious properties was examined. One hundred seventy-two 14-month-olds and 22-month-olds were presented with novel target objects followed by test objects that varied in similarity to the target. Objects were introduced with a novel word or a novel gesture or with no label. When target and test objects were highly similar in shape, both 14- and 22-month-olds inferred that these objects shared a nonobvious property, regardless of whether the objects were labeled with a word or a gesture or with no label. When objects were labeled with the same word, both 14- and 22-month-olds generalized the nonobvious properties to objects that shared minimal perceptual similarity. Finally, when objects were labeled with the same gesture, 14-month-olds, but not 22-month-olds, generalized the nonobvious properties to objects that shared minimal perceptual similarity. These results indicate that 14-month-olds possess a more generalized symbolic system as they will rely on both words and gestures to guide their inferences. By 22-months of age, infants treat words as a privileged referential form when making inductive inferences. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

THE IMPORTANCE OF NEUTRAL EXAMPLES FOR LEARNING SENTIMENT

Most research on learning to identify sentiment ignores "neutral" examples, learning only from examples of significant (positive or negative) polarity. We show that it is crucial to use neutral examples in learning polarity for a variety of reasons. Learning from negative and positive examples alone will not permit accurate classification of neutral examples. Moreover, the use of neutral training examples in learning facilitates better distinction between positive and negative examples.     

基于类间分散度和类内集中度的文本特征选择。

特征选择是文本分类的关键步骤之一,所选特征子集的优劣直接影响文本分类的结果。论文首先定义了两种特征分类能力：一种是特征对类间文档的分散程度,该分散度越大越好;另一种是特征对类内文档的聚集程度,该集中度越大越好。然后把这两种特征影响度有机地结合起来设计了一个新的特征选择方法,该方法能够对所选特征进行综合考虑,从而使获得的特征集具有较好的代表性。仿真实验表明所提特征选择方法在一定程度上能够提高文本分类性能。     

基于蜂群算法的移动agent迁移策略研究*

旅行agent问题是一个复杂的组合优化问题,在于解决保证agent在不同主机间移动时如何根据任务情况规划路线,实现在完成任务时间最短的路线上迁移。采用进化算法的搜索求解具有启发性强、鲁棒性强的特点,但也面临着陷于局部最优解,导致agent在迁移过程中整体任务完成效率降低等问题。提出了一种基于蜂群算法的agent迁移模型,将agent划分为侦察、引领和跟随三种角色,agent通过彼此间的信息互换,在群体迁移的过程中同时实施新路径的发现和调整。实验结果表明,该算法可以很好地实现目标寻径效率,与经典蚁群算法相比     

基于混沌微粒群算法的文本分类研究*

针对中文文本分类问题,将其用于分类规则的抽取。为了避免微粒群算法在全局优化中陷入局部极值,利用混沌运动遍历性、随机性等特点,对标准微粒群算法进行了改进,提出了基于混沌微粒群算法的文本自动分类方法。仿真实验表明本算法对文档进行分类是一种比较可行的分类方法,分类精度高、速度快。     

浅谈基于粗集理论的文本归类系统

文本归类是处理大量文本数据自动分类的重要技术。基于粗集理论建立的林业文本信息归类系统,是在已知类别的训练集的基础上,通过分析训练数据样本,建立决策表产生区分矩阵构造出区分函数,并化简它,得到最小属性约简,最后应用Apriori算法产生最终分类的规则表,利用产生的规则表,可将林业文本信息数据进行自动归类。