Categorization by Groups

Categorization is a core psychological process central to consumer and managerial decision-making. While a substantial amount of research has been conducted to examine individual categorization behaviors, relatively little is known about the group categorization process. In two experiments, we demonstrate that group categorization differs systematically from that of individuals: groups created a larger number of categories with fewer items in each category. This effect is mediated by groups’ larger knowledge base and moderated by groups’ ease in achieving consensus. While neither broader nor narrower categories are normatively superior, more integration or distinction among concepts may be desirable for a given objective. Thus, it is important for those relying on the outputs of categorization tasks, such as web site designers, store managers, product development teams, and product marketing managers, to understand and consider the systematic differences between group and individual categorization.Decision-making;Categorization;Group and Individual Categorization

Exploring Topic-based Language Models for Effective Web Information Retrieval

The main obstacle for providing focused search is the relative opaqueness of search request -- searchers tend to express their complex information needs in only a couple of keywords. Our overall aim is to find out if, and how, topic-based language models can lead to more effective web information retrieval. In this paper we explore retrieval performance of a topic-based model that combines topical models with other language models based on cross-entropy. We first define our topical categories and train our topical models on the .GOV2 corpus by building parsimonious language models. We then test the topic-based model on TREC8 small Web data collection for ad-hoc search.Our experimental results show that the topic-based model outperforms the standard language model and parsimonious model

Documenting the adverse impact of resume screening: degree of ethnic identification matters

We investigated adverse impact of resume screening taking into account the intersectionality of minority characteristics. A correspondence audit test showed hiring discrimination depended on the strength of applicants’ ethnic identification. The odds for rejection were 4-6 times higher for resumes with ethnic minority identifiers (Arab names; Arab affiliations) when compared to ethnic majority identifiers (Dutch names; Dutch affiliations). Sex moderated the ethnicity effect but the particular effect (ethnic prominence; double jeopardy against females or males) depended on the type and degree of ethnic identification, lending support for a within-category approach to study ethnic prejudice. The four-fifths rule resulted in similar findings. Theoretical implications regarding the intersectional effects of minority characteristics and practical implications regarding ways to avert adverse impact during resume-screening are discussed

Expert Finding by Capturing Organisational Knowledge from Legacy Documents

Organisations capitalise on their best knowledge through the improvement of shared expertise which leads to a higher level of productivity and competency. The recognition of the need to foster the sharing of expertise has led to the development of expert finder systems that hold pointers to experts who posses specific knowledge in organisations. This paper discusses an approach to locating an expert through the application of information retrieval and analysis processes to an organization’s existing information resources, with specific reference to the engineering design domain. The approach taken was realised through an expert finder system framework. It enables the relationships of heterogeneous information sources with experts to be factored in modelling individuals’ expertise. These valuable relationships are typically ignored by existing expert finder systems, which only focus on how documents relate to their content. The developed framework also provides an architecture that can be easily adapted to different organisational environments. In addition, it also allows users to access the expertise recognition logic, giving them greater trust in the systems implemented using this framework. The framework were applied to real world application and evaluated within a major engineering company

Variation of word frequencies across genre classification tasks

This paper examines automated genre classification of text documents and its role in enabling the effective management of digital documents by digital libraries and other repositories. Genre classification, which narrows down the possible structure of a document, is a valuable step in realising the general automatic extraction of semantic metadata essential to the efficient management and use of digital objects. In the present report, we present an analysis of word frequencies in different genre classes in an effort to understand the distinction between independent classification tasks. In particular, we examine automated experiments on thirty-one genre classes to determine the relationship between the word frequency metrics and the degree of its significance in carrying out classification in varying environments