Cookies
O website necessita de alguns cookies e outros recursos semelhantes para funcionar. Caso o permita, o INESC TEC irá utilizar cookies para recolher dados sobre as suas visitas, contribuindo, assim, para estatísticas agregadas que permitem melhorar o nosso serviço. Ver mais
Aceitar Rejeitar
  • Menu
Publicações

Publicações por Carla Lopes

2017

Using the Characteristics of Documents, Users and Tasks to Predict the Situational Relevance of Health Web Documents

Autores
Oroszlányová, M; Lopes, CT; Nunes, S; Ribeiro, C;

Publicação
Journal of Information Systems Engineering & Management

Abstract

2017

FEUP at TREC 2017 OpenSearch Track Graph-Based Models for Entity-Oriented

Autores
Devezas, JL; Lopes, CT; Nunes, S;

Publicação
Proceedings of The Twenty-Sixth Text REtrieval Conference, TREC 2017, Gaithersburg, Maryland, USA, November 15-17, 2017

Abstract

2018

The influence of document characteristics on the quality of health web documents

Autores
Oroszlanyova, M; Lopes, CT; Nunes, S; Ribeiro, C;

Publicação
2018 13TH IBERIAN CONFERENCE ON INFORMATION SYSTEMS AND TECHNOLOGIES (CISTI)

Abstract
The quality of consumer-oriented health information on the Web is usually assessed through the medical certification of websites. These tools are built upon quality indicators but, so far, no standard set of indicators has been defined. The objective of the present study is to explore the popularity of specific document features and their influence on the quality of health web documents, using HON code as ground truth. A set of top-ranked health documents retrieved from a major search engine was characterized in a univariate analysis, and then used in a bivariate analysis to seek features that affect documents' quality. The univariate analysis provides insights into the characteristics of the overall population of the health web documents. The bivariate analysis reveals strong relations between documents' quality and a set of features (namely split content, videos, images, advertisements, English language) that are potential quality indicators. We characterized health web documents and identified specific document features that can be used to assess whether the information in such documents is trustworthy. The main contribution of this work is to provide other features as candidate indicators of quality. Non-health professionals can use these indicators in automatic and manual assessments of health content.

2018

Can user and task characteristics be used as predictors of success in health information retrieval sessions?

Autores
Oroszlanyova, M; Lopes, CT; Nunes, S; Ribeiro, C;

Publicação
INFORMATION RESEARCH-AN INTERNATIONAL ELECTRONIC JOURNAL

Abstract
Introduction. The concept and study of relevance has been a central subject in information science. Although research in information retrieval has been focused on topical relevance, other kinds of relevance are also important and justify further study. Motivational relevance is typically inferred by criteria such as user satisfaction and success. Method. Using an existing dataset composed by an annotated set of health Web documents assessed for relevance and comprehension by a group of users, we build a multivariate prediction model for the motivational relevance of search sessions. Analysis. The analysis was based on lasso variable selection, followed by model selection using multiple logistic regression. Results. We have built two regression models; the full model, which considers all variables of the dataset, has a lower estimated prediction error than the reduced model, which contains the statistically-significant variables from the full model. The higher values of evaluation metrics, including accuracy, specificity and sensitivity in the full model support this finding. The full model has an accuracy of 91.94%, and is better at predicting motivational relevance. Conclusions. Our findings suggest features that can be considered by search engines to estimate motivational relevance, to be used in addition to topical relevance. Among these features, a high level of success in Web search and in health information search on social networks and chats are some of the most influencing user features. This shows that users with higher computer literacy might feel more satisfied and successful after completing the search tasks. In terms of task features, the results suggest that users with clearer goals feel more successful. Moreover, results show that users would benefit from the help of the system in clarifying the retrieved documents.

2018

Supporting Description of Research Data: Evaluation and Comparison of Term and Concept Extraction Approaches

Autores
Monteiro, C; Lopes, CT; Silva, JR;

Publicação
DIGITAL LIBRARIES FOR OPEN KNOWLEDGE, TPDL 2018

Abstract
The importance of research data management is widely recognized. Dendro is an ontology-based platform that allows researchers to describe datasets using generic and domain-specific descriptors from ontologies. Selecting or building the right ontologies for each research domain or group requires meetings between curators and researchers in order to capture the main concepts of their research. Envisioning a tool to assist curators through the automatic extraction of key concepts from research documents, we propose 2 concept extraction methods and compare them with a term extraction method. To compare the three approaches, we use as ground truth an ontology previously created by human curators.

2018

Effects of Language and Terminology of Query Suggestions on the Precision of Health Searches

Autores
Lopes, CT; Ribeiro, C;

Publicação
Experimental IR Meets Multilinguality, Multimodality, and Interaction - 9th International Conference of the CLEF Association, CLEF 2018, Avignon, France, September 10-14, 2018, Proceedings

Abstract
Health information is highly sought on the Web by users that naturally have different levels of expertise in the topics they search for. Assisting users with query formulation is important when users are searching for topics about which they have little knowledge or familiarity. To assist users with health query formulation, we developed a query suggestion system that provides alternative queries combining Portuguese and English language with lay and medico-scientific terminology. Here, we analyze how this system affects the precision of search sessions. Results show that a system providing these suggestions tends to perform better than a system without them. On specific groups of users, clicking on suggestions has positive effects on precision while using them as sources of new terms has the opposite effect. This suggests that a personalized suggestion system might have a good impact on precision. © Springer Nature Switzerland AG 2018.

  • 4
  • 14