Cross-lingual document retrieval categorisation and navigation based on distributed services

The widespread use of the Internet across countries has increased the need for access to document collections that are often written in languages different from a user’s native language. In this paper we describe Clarity, a Cross Language Information Retrieval (CLIR) system for English, Finnish, Swedish, Latvian and Lithuanian. Clarity is a fully-fledged retrieval system that supports the user during the whole process of query formulation, text retrieval and document browsing. We address four of the major aspects of Clarity: (i) the user-driven methodology that formed the basis for the iterative design cycle and framework in the project, (ii) the system architecture that was developed to support the interaction and coordination of Clarity’s distributed services, (iii) the data resources and methods for query translation, and (iv) the support for Baltic languages. Clarity is an example of a distributed CLIR system built with minimal translation resources and, to our knowledge, the only such system that currently supports Baltic languages

The arguments of associations

This chapter considers associative solutions to “non‐linear” discrimination problems, such as negative patterning (A+ and B+ vs AB‐) and the biconditional discrimination (AB+ and CD+ vs AC‐ and BD‐). It is commonly assumed that the solution to these discriminations requires “configural” elements that are added to the compound of two stimuli. However, these discriminations can be solved by assuming that some elements of each stimulus are suppressed when two stimuli are presented in compound. Each of these approaches can solve patterning and biconditional discriminations because they allow some elements, as the arguments of associations, to have differential “presence” on reinforced versus nonreinforced trials, and thus differential associability and control over responding. The chapter then presents a more specific version of one of these models, describing how interactions between stimuli, particularly the competition for attention, provide a mechanism whereby some elements are more suppressed than others when stimuli are presented simultaneously as a compound

An Initial Investigation into Fixed and Adaptive Stopping Strategies

Most models, measures and simulations often assume that a searcher will stop at a predetermined place in a ranked list of results. However, during the course of a search session, real-world searchers will vary and adapt their interactions with a ranked list. These interactions depend upon a variety of factors, including the content and quality of the results returned, and the searcher's information need. In this paper, we perform a preliminary simulated analysis into the influence of stopping strategies when query quality varies. Placed in the context of ad-hoc topic retrieval during a multi-query search session, we examine the influence of fixed and adaptive stopping strategies on overall performance. Surprisingly, we find that a fixed strategy can per- form as well as the examined adaptive strategies, but the fixed depth needs to be adjusted depending on the querying strategy used. Further work is required to explore how well the stopping strategies affect actual search behaviour, and to determine whether one stopping strategy is dominant

Personalization Needs Extension towards Task Stages in Collaborative Research Work Tasks

People have various goals and intentions while interacting with information items. The intentions are derived from larger, motivating work tasks, and the intentions during the stages of task performance may vary. Further, the information activities are often conducted in collaborative work settings. We present preliminary findings on a collaborative research task process indicating that task-stages are also observable in collaborative tasks. We show that trust building and common understanding of the task emerge and discuss the implications to evaluation of information interaction. Based on our understanding, we claim that the personalization based on individuals’ past actions is not necessarily enough, but needs extension towards task stages in collaborative research tasks

Adaptation Strategies: A Comparison between E-Learning and E-Commerce Techniques

Part 2: First AI in Education Workshop: Innovations and Applications (AIeIA 2012)International audienceThe importance of e-learning and e-commerce applications has significantly increased in the past few years. Seeking better design and implementation principles is a research goal with, potentially, a significant impact. One of the commonalities of both applications is user-centricity. Understanding user behavior is critical especially in user-centered applications such as e-commerce and e-learning. In this work we discuss some of the fundamental similarities and differences in e-commerce and formal e-learning adaptation and discuss lessons that could be learned. We argue that current user pattern mining techniques should take into account behavioral and educational theories for distance learning in order to be efficient

Cumulated Relative Position: A Metric for Ranking Evaluation

The development of multilingual and multimedia informa- tion access systems calls for proper evaluation methodologies to ensure that they meet the expected user requirements and provide the desired effectiveness. In this paper, we propose a new metric for ranking evaluation, the CRP