Effects of classification context on categorization in natural categories

The patterns of classification of borderline instances of eight common taxonomic categories were examined under three different instructional conditions to test two predictions: first, that lack of a specified context contributes to vagueness in categorization, and second, that altering the purpose of classification can lead to greater or lesser dependence on similarity in classification. The instructional conditions contrasted purely pragmatic with more technical/quasi-legal contexts as purposes for classification, and these were compared with a no-context control. The measures of category vagueness were between-subjects disagreement and within-subjects consistency, and the measures of similarity based categorization were category breadth and the correlation of instance categorization probability with mean rated typicality, independently measured in a neutral context. Contrary to predictions, none of the measures of vagueness, reliability, category breadth, or correlation with typicality were generally affected by the instructional setting as a function of pragmatic versus technical purposes. Only one subcondition, in which a situational context was implied in addition to a purposive context, produced a significant change in categorization. Further experiments demonstrated that the effect of context was not increased when participants talked their way through the task, and that a technical context did not elicit more all-or-none categorization than did a pragmatic context. These findings place an important boundary condition on the effects of instructional context on conceptual categorization

Hypotheses, evidence and relationships: The HypER approach for representing scientific knowledge claims

Biological knowledge is increasingly represented as a collection of (entity-relationship-entity) triplets. These are queried, mined, appended to papers, and published. However, this representation ignores the argumentation contained within a paper and the relationships between hypotheses, claims and evidence put forth in the article. In this paper, we propose an alternate view of the research article as a network of 'hypotheses and evidence'. Our knowledge representation focuses on scientific discourse as a rhetorical activity, which leads to a different direction in the development of tools and processes for modeling this discourse. We propose to extract knowledge from the article to allow the construction of a system where a specific scientific claim is connected, through trails of meaningful relationships, to experimental evidence. We discuss some current efforts and future plans in this area

Improving spam filtering by combining Naive Bayes with simple k-nearest neighbor searches

Using naive Bayes for email classification has become very popular within the last few months. They are quite easy to implement and very efficient. In this paper we want to present empirical results of email classification using a combination of naive Bayes and k-nearest neighbor searches. Using this technique we show that the accuracy of a Bayes filter can be improved slightly for a high number of features and significantly for a small number of features

Topic Detection and Tracking in Personal Search History

This thesis describes a system for tracking and detecting topics in personal search history. In particular, we developed a time tracking tool that helps users in analyzing their time and discovering their activity patterns. The system allows a user to specify interesting topics to monitor with a keyword description. The system would then keep track of the log and the time spent on each document and produce a time graph to show how much time has been spent on each topic to be monitored. The system can also detect new topics and potentially recommend relevant information about them to the user. This work has been integrated with the UCAIR Toolbar, a client side agent. Considering limited resources on the client side, we designed an e????cient incremental algorithm for topic tracking and detection. Various unsupervised learning approaches have been considered to improve the accuracy in categorizing the user log into appropriate categories. Experiments show that our tool is effective in categorizing the documents into existing categories and detecting the new useful catgeories. Moreover, the quality of categorization improves over time as more and more log is available

Correcting political and consumer misperceptions

While fact-checking has grown dramatically in the last decade, little is known about the relative effectiveness of different formats in correcting false beliefs or overcoming partisan resistance to new information. This article addresses that gap by using theories from communication and psychology to compare two prevailing approaches: An online experiment examined how the use of visual “truth scales” interacts with partisanship to shape the effectiveness of corrections. We find that truth scales make fact-checks more effective in some conditions. Contrary to theoretical predictions and the fears of some journalists, their use does not increase partisan backlash against the correction or the organization that produced it