Universal Mobile Information Retrieval

International audienceThe 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

Textual entailment by generality

Available online 7 December 2011International audienceTextual Entailment consists in determining if an entailment relation exists between two texts. In this paper, we present an Informative Asymmetric Measure called the Asymmetric InfoSimba (AIS), which we combine with different asym-metric association measures to recognize the specific case of Textual Entailment by Generality. In particular, the AIS proposes an unsupervised, language-independent, threshold free solution. This new measure is tested against the first Recognizing Textual Entailment dataset for an exhaustive number of asymmetric association measures and shows that the combination of the AIS with the Braun-Blanket steadily improves results against competitive measures such as the one proposed by [1]

Merged agreement algorithms for domain independent sentiment analysis

Available online 7 December 2011International audienceIn this paper, we consider the problem of building models that have high sentiment classification accuracy across domains. For that purpose, we present and evaluate three new algorithms based on multi-view learning using both high-level and low-level views, which show improved results compared to the state-of-the-art SAR algorithm [1] over cross-domain text subjectivity classification. Our experimental results present accuracy levels of 80% with two views, combining SVM classifiers over high-level features and unigrams compared to 77.1% for the SAR algorithm

Recognizing Textual Entailment by Generality using Informative Asymmetric Measures to Summarize Ephemeral Clusters

International audienceIn 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

Detection of Extreme Sentiments on Social Networks with BERT

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Improving Mental Disorder Predictions using Feature-Based Machine Learning Techniques

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Artificial Intelligence on Mental Health Context

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