In this paper, we describe an approach for tweet contextualization developed in the context of the INEX 2012. The task was to provide a context up to 500 words to a tweet from the Wikipedia. As a baseline system, we used TF-IDF cosine similarity measure enriched by smoothing from local context, named entity recognition and part-of-speech weighting presented at INEX 2011. We modified this method...

Discovering correspondences between schema elements is a crucial task for data integration. Most matching tools are semi-automatic, e.g. an expert must tune some parameters (thresholds, weights, etc.). They mainly use several methods to combine and aggregate similarity measures. However, their quality results often decrease when one requires to integrate a new similarity measure or when matchin...

Case retrieval constitutes an interesting area of research which contributes to the evolution of several domains. The similarity measure module is a fundamental step in the retrieval process which affects remarkably on a retrieval system. In this context, we suggest in this paper a similarity measure applied to brain tumor cases retrieval. The rationale behind the proposed measure consists in q...

In contrast to conventional documents, a Web document consists of a number of tags which provide hints on the structure of the documents. In this paper, we propose a Web-document retrieval method using the characteristics of HTML tags. This method learns the importance of tags from a training text set. We use a genetic algorithm for learning the importance weights. We also present a modi ed sim...

The main goal of data aggregation technique is to gather data in energy manner for a long-term network monitoring. In data aggregation technique, the role of an aggregator is to collect sensed data from surrounding environment and transmit the collected data to base station. One part of data aggregation process is applying similarity function in order to minimize redundancy from the raw data an...

In this paper, we present a new inductive learning method for multilabel text categorization. The proposed method uses a mutual information measure to select terms and constructs document descriptor vectors for each category based on these terms. These document descriptor vectors form a document descriptor matrix. It also uses the document descriptor vectors to construct a document-similarity m...

Image registration under challenging realistic conditions is a very important area of research. In this paper, we focus on algorithms that seek to densely align two volumetric images according to a global similarity measure. Despite intensive research in this area, there is still a need for similarity measures that are robust to outliers common to many different types of images. For example, me...

We study the problem of mapping a large indoor environment using an omnivideo camera. Local features from omnivideo images and epipolar geometry are used to compute the relative pose between pairs of images. These poses are then used in an Extended Information Filter using a trajectory based representation where only the robot poses corresponding to captured images are reconstructed. The featur...

The goal of the current paper is to introduce a novel clustering algorithm that has been designed for grouping transcribed textual documents obtained out of audio, video segments. Since audio transcripts are normally highly erroneous documents, one of the major challenges at the text processing stage is to reduce the negative impacts of errors gained at the speech recognition stage. Other diffi...

In this paper, we developed a deep neural network (DNN) that learns to solve simultaneously the three tasks of the cQA challenge proposed by the SemEval-2016 Task 3, i.e., question-comment similarity, question-question similarity and new question-comment similarity. The latter is the main task, which can exploit the previous two for achieving better results. Our DNN is trained jointly on all th...