Contextualizing the blogosphere: A comparison of traditional and novel user interfaces for the web

In this paper, we investigate how contextual user interfaces affect blog reading experience. Based on a review of previous research, we argue why and how contextualization may result in (H1) enhanced blog reading experiences. In an eyetracking experiment, we tested 3 different web-based user interfaces for information spaces. The StarTree interface (by Inxight) and the Focus-Metaphor interface are compared with a standard blog interface. Information tasks have been used to evaluate and compare task performance and user satisfaction between these three interfaces. We found that both contextual user interfaces clearly outperformed the traditional blog interface, both in terms of task performance as well as user satisfaction. © 2007 Laqua, S., Ogbechie, N. and Sasse, M. A

Context-Aware Stemming algorithm for semantically related root words

There is a growing interest in the use of context-awareness as a technique for developing pervasive computing applications that are flexible and adaptable for users. In this context, however, information retrieval (IR) is often defined in terms of location and delivery of documents to a user to satisfy their information need. In most cases, morphological variants of words have similar semantic interpretations and can be considered as equivalent for the purpose of IR applications. Consequently, document indexing will also be more meaningful if semantically related root words are used instead of stems. The popular Porter’s stemmer was studied with the aim to produce intelligible stems. In this paper, we propose Context-Aware Stemming (CAS) algorithm, which is a modified version of the extensively used Porter’s stemmer. Considering only generated meaningful stemming words as the stemmer output, the results show that the modified algorithm significantly reduces the error rate of Porter’s algorithm from 76.7% to 6.7% without compromising the efficacy of Porter’s algorithm

Búsquedas web con información de contexto y anotaciones sociales

Una de las tareas más comunes que realizan los usuarios en la Web es la búsqueda de información utilizando motores de búsqueda tradicionales. Generalmente, éstas se basan en un conjunto de términos que son tratados fuera de contexto alguno. La incorporación de información contextual permite obtener resultados más precisos y puede ser presentada al sistema de formas diferentes. Una fuente posible son los sistemas basados en “anotaciones sociales”, los cuales se enriquecen con la participación de los usuarios para organizar información. En este trabajo se trata el problema de las búsquedas en contexto de literatura científica. En particular, se propone la utilización de un artículo científico como información de contexto para una consulta dada. Además, ésta se complementa con la incorporación de anotaciones sociales provenientes de etiquetas asociadas a los artículos, para ser utilizada en un motor de búsqueda de propósito general existente. Se presenta una propuesta, resultados preliminares y el estado actual de la investigación.Eje: Arquitectura, Redes y Sistemas OperativosRed de Universidades con Carreras en Informática (RedUNCI

Búsquedas web con información de contexto y anotaciones sociales

Una de las tareas más comunes que realizan los usuarios en la Web es la búsqueda de información utilizando motores de búsqueda tradicionales. Generalmente, éstas se basan en un conjunto de términos que son tratados fuera de contexto alguno. La incorporación de información contextual permite obtener resultados más precisos y puede ser presentada al sistema de formas diferentes. Una fuente posible son los sistemas basados en “anotaciones sociales”, los cuales se enriquecen con la participación de los usuarios para organizar información. En este trabajo se trata el problema de las búsquedas en contexto de literatura científica. En particular, se propone la utilización de un artículo científico como información de contexto para una consulta dada. Además, ésta se complementa con la incorporación de anotaciones sociales provenientes de etiquetas asociadas a los artículos, para ser utilizada en un motor de búsqueda de propósito general existente. Se presenta una propuesta, resultados preliminares y el estado actual de la investigación.Eje: Arquitectura, Redes y Sistemas OperativosRed de Universidades con Carreras en Informática (RedUNCI

Navigating Haystacks at 70 mph: Intelligent Search for Intelligent In-Car Services

ABSTRACT With an explosion of in-car services, it has become not only difficult but unsafe for drivers to search and access large amounts of information using current interaction paradigms. In this paper, we present a novel approach for visualizing and exploring search results, and the potential benefits of its application to the current in-car environment. We have iteratively developed and tested a prototype system that enables the seamless and personalized exploration of information spaces. In a number of eye-tracking studies, we analyzed user satisfaction and task performance for factual and explorative search tasks. We found that most participants were faster, made fewer errors and found the system easier to use than traditional ones. We believe that this approach would improve the traditional in-car interfaces -to search and access large number of services with rich information. This would reduce driver inattention to the road and improve road safety

The Acquisition Of Lexical Knowledge From The Web For Aspects Of Semantic Interpretation

This work investigates the effective acquisition of lexical knowledge from the Web to perform semantic interpretation. The Web provides an unprecedented amount of natural language from which to gain knowledge useful for semantic interpretation. The knowledge acquired is described as common sense knowledge, information one uses in his or her daily life to understand language and perception. Novel approaches are presented for both the acquisition of this knowledge and use of the knowledge in semantic interpretation algorithms. The goal is to increase accuracy over other automatic semantic interpretation systems, and in turn enable stronger real world applications such as machine translation, advanced Web search, sentiment analysis, and question answering. The major contributions of this dissertation consist of two methods of acquiring lexical knowledge from the Web, namely a database of common sense knowledge and Web selectors. The first method is a framework for acquiring a database of concept relationships. To acquire this knowledge, relationships between nouns are found on the Web and analyzed over WordNet using information-theory, producing information about concepts rather than ambiguous words. For the second contribution, words called Web selectors are retrieved which take the place of an instance of a target word in its local context. The selectors serve for the system to learn the types of concepts that the sense of a target word should be similar. Web selectors are acquired dynamically as part of a semantic interpretation algorithm, while the relationships in the database are useful to iii stand-alone programs. A final contribution of this dissertation concerns a novel semantic similarity measure and an evaluation of similarity and relatedness measures on tasks of concept similarity. Such tasks are useful when applying acquired knowledge to semantic interpretation. Applications to word sense disambiguation, an aspect of semantic interpretation, are used to evaluate the contributions. Disambiguation systems which utilize semantically annotated training data are considered supervised. The algorithms of this dissertation are considered minimallysupervised; they do not require training data created by humans, though they may use humancreated data sources. In the case of evaluating a database of common sense knowledge, integrating the knowledge into an existing minimally-supervised disambiguation system significantly improved results – a 20.5% error reduction. Similarly, the Web selectors disambiguation system, which acquires knowledge directly as part of the algorithm, achieved results comparable with top minimally-supervised systems, an F-score of 80.2% on a standard noun disambiguation task. This work enables the study of many subsequent related tasks for improving semantic interpretation and its application to real-world technologies. Other aspects of semantic interpretation, such as semantic role labeling could utilize the same methods presented here for word sense disambiguation. As the Web continues to grow, the capabilities of the systems in this dissertation are expected to increase. Although the Web selectors system achieves great results, a study in this dissertation shows likely improvements from acquiring more data. Furthermore, the methods for acquiring a database of common sense knowledge could be applied in a more exhaustive fashion for other types of common sense knowledge. Finally, perhaps the greatest benefits from this work will come from the enabling of real world technologies that utilize semantic interpretation