Sebastião Pais

Universal Mobile Information Retrieval

The shift in human computer interaction from desktop computing to mobile interaction highly influences the needs for new designed interfaces. In this paper, we address the issue of searching for information on mobile devices, an area also known as Mobile Information Retrieval. In particular, we propose to summarize as much as possible the information retrieved by any search engine to allow universal access to information.

Using Accuracy Analysis to Find the Best Classifier for Intelligent Personal Assistants

Abstract An Intelligent Personal Assistant (IPA) is an agent that has the purpose of helping the user with his daily tasks. This paper is focused on IPAs for Internet of Things (IoT) environments. In this sense, a good IPA has the capability of surveying his user behaviour and suggest tasks or make decisions with the intention of simplifying the user interaction with his surroundings. With this in mind, this paper focuses on studying the accuracy of various classifiers, with the objective of finding the one that suits better the needs of an IPA for IoT. The aim is to test each algorithm with a dataset of events, that relate to past behaviours of the user, and find if there is an opportunity to notify the user that he/she may want to take an action or create an automation based on the learned behaviour.

Recognizing Textual Entailment by Generality Using Informative Asymmetric Measures and Multiword Unit Identification to Summarize Ephemeral Clusters

In the context of Ephemeral Clustering of web Pages, it can be interesting to label each cluster with a small summary instead of just a label. Within this scope, we introduce the paradigm of Textual Entailment by Generality, which can be defined as the entailment from a specific web snippet towards a more general web snippet. The subjacent idea is to find the best web snippet, which summarizes and subsumes all the other web snippets within an ephemeral cluster. To reach this objective, we first propose a new informative asymmetric similarity measure called the Simplified Asymmetric InfoSimba(AISs), which can be combined with different asymmetric association measures. In particular, the AISs proposes an unsupervised language-independent solution to infer Textual Entailment by Generality and as such can help to encounter the web snippet with maximum semantic coverage. This new methodology is tested against the first Recognizing Textual Entailment data set (RTE-1)1 for an exhaustive number of asymmetric association measures with and without the identification of Multiword Units. The comparative experiments with existing state-of-the-art methodologies show promising results.