Publications

Publications by LIAAD

2017

Curvas de crescimento dos portugueses dos 3 aos 7 anos: peso, estatura e índice de massa corporal

Authors
Freitas, D,; Poínhos, Rui; Sousa, Bruno; Franchini, Bela; Afonso, Cláudia; Correia, Flora; Almeida, Maria Daniel Vaz de; Oliveira, B.M.P.M.;

Publication

Abstract

2017

Evolução da insulinorresistência em doentes submetidos a cirurgia bariátrica

Authors
Areias, M.; Neves, O.; Poínhos, Rui; Bruno M P M Oliveira; Correia, Flora;

Publication

Abstract

2017

Breastfeeding and nutritional status in a population between 6 and 18 years old

Authors
Sousa, B.; Pinto, C.; Oliveira, Bruno M.P.M.; Almeida, Maria Daniel Vaz de;

Publication

Abstract

2017

Birth weight, parents' body mass index and education level and nutritional status in a population between 6 and 18 years old

Authors
Sousa, B.; Pinto, C.; Oliveira, Bruno M.P.M.; Almeida, Maria Daniel Vaz de;

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Abstract

2017

Prevalência e determinantes da obesidade, gordura abdominal, e risco cardiovascular numa amostra representativa de idosos portugueses

Authors
Algarinho, J.; Afonso, Cláudia; Poínhos, Rui; Franchini, Bela; Pinhão, Sílvia; Correia, Flora; Almeida, Maria Daniel Vaz de; Bruno M P M Oliveira;

Publication

Abstract

2017

Learning Temporal Ambiguity in Web Search Queries

Authors
Mansouri, B; Zahedi, MS; Rahgozar, M; Oroumchian, F; Campos, R;

Publication
CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT

Abstract
Time has strong influence on web search. The temporal intent of the searcher adds an important dimension to the relevance judgments of web queries. However, lack of understanding their temporal requirements increases the ambiguity of the queries, turning retrieval effectiveness improvements into a complex task. In this paper, we propose an approach to classify web queries into four different categories considering their temporal ambiguity. For each query, we develop features from its search volumes and related queries using Google trends and its related top Wikipedia pages. Our experiment results show that these features can determine temporal ambiguity of a given query with high accuracy. We have demonstrated that a Multilayer Perceptron Networks can achieve better results in classifying temporal class of queries in comparison to other classifiers.

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