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In this work we suggest a model according to which semantics has been already generated during the perception through the interaction of three dynamic levels of perceptual organization. We consider perceptual grouping as the first order processing. Shape formation is considered as the second order processing. Both grouping and shape formation can be considered as two complementary and interrelated...
The web is one of the most important mean of disseminating maps to users worldwide. These maps are commonly generated, on-the-fly, by extracting and displaying pre-processed data which is stored beforehand in specific databases. Though rapid, this approach lacks flexibility and does not necessarily provide the user with all the relevant spatial data at the right time and in the right way. Indeed,...
With the fast growing development of the Web, the adoption of ontologies to improve the exploitation of information resources, is already heralded as a promising model of representation. However, the relevance of information that they contain requires regular updating, and specifically, the addition of new knowledge. Recently, new research approaches were defined in order to automatically enrich ontology...
The lack of a software tool to automate the creation of a well-defined, Natural Language Software Requirements Specification (SRS) document is an obstacle to the process of efficient Requirements Engineering (RE). This paper provides an overview of a methodology and a novel software tool that attempt to formalize and automate the RE process, and it expands on the use of the SRS Documentation component...
We argue that pronominal anaphora understanding must rely on the recovery of argument structure asymmetries in conjunction with principles restricting the set of possible antecedents for pronouns. We provide empirical evidence for the need of deep parsing recovering arguments, both overt and covert, that can be possible antecedents for pronouns. We identify several limits of systems that do not rely...
Situation awareness is a promising approach to recommend to a mobile user the most suitable resources for a specific situation. However, determining the correct user situation is not a simple task since users have different habits that may affect the way in which the situations arise. Thus, an appropriate tuning aimed at adapting the situation recognizer to the specific user is desirable to make a...
Bag of visual patches (BOP) image representation has been the main research topic in computer vision literature for scene and object recognition tasks. Building visual vocabularies from local image feature vectors extracted automatically from images have direct effect on producing discriminative visual patches. Local image features hold important information of their locations in the image which are...
In this paper, we present an approach to automatically extract and classify opinions in texts. We propose a similarity measurement calculating semantically distances between a word and predefined subgroups of seed words. We have evaluated our algorithm on the semantic evaluation company “SemEval 2007” corpus, and we obtained the best value of Precision and F1 62% and 61%. As an improvement of 20 %...
Large databases with uncertainty became more common in many applications. Ranking queries are essential tools to process these databases and return only the most relevant answers of a query, based on a scoring function. Many approaches were proposed to study and analyze the problem of efficiently answering such ranking queries. Managing distributed uncertain database is also an important issue. In...
In this work, we propose an approach of thematisation of audiovisual (AV) documents for a research according to topics evoked in each document. The first step of our approach is to define the descriptive metadata allowing a bibliographical description of the whole documents. The second step is divided into three stages: the first one is a temporal segmentation, the second one is space segmentation...
The constant growth of the Internet has made recommender systems very useful to guide users coping with a large amount of data. In this paper, we present a domain independent collaborative and semantic-based recommender system which uses distinct and complementary modules. The approach targets users with various interests and is based on: (i) a collaborative module using association rules in order...
Word Alignment is an important supporting task for different NLP applications like training of machine translation systems, translation lexicon induction, word sense discovery, word sense disambiguation, information extraction and the cross-lingual projection of linguistic information. In this paper we study the main rules and guidelines required to build an aligner tool for Arabic language which...
Current semantic recommender systems aim to exploit the website ontologies to produce valuable web recommendations. However, Web usage knowledge for recommendation is presented separately and differently from the domain ontology, this leads to the complexity of using inconsistent knowledge resources. This paper aims to solve this problem by proposing a novel ontology-style model of Web usage to represent...
Nowadays, satisfying user needs has become the main challenge in a variety of web applications. Recommender systems play a major role in that direction. However, as most of the information is present in a textual form, recommender systems face the challenge of efficiently analyzing huge amounts of text. The usage of semantic-based analysis has gained much interest in recent years. The emergence of...
The paper is devoted to intelligent matching ontological subgraphs to concepts. The problem is considered from the point of view of rough set theory. An accuracy of approximation determines how far in a semantic space it is from a given ontological subgraph to a given concept. The approach presented in the paper can be applied to intelligent searching of a bibliographical data base for abstracts semantically...
In automatic image annotation, it is often extracting low-level visual features from original image for the purpose of mapping to high level image semantic information. In this paper, we propose a novel method which integrates kernel independent component analysis (KICA) and support vector machine (SVM) for analyzing the semantic information of natural images. KICA, which contains a nonlinear kernel...
In order to improve the classifier performance in semantic image annotation, we propose a novel method which adopts learning vector quantization (LVQ) technique to optimize low level feature data extracted from given image. Some representative vectors are selected with LVQ to train support vector machine (SVM) classifier instead of using all feature data. Performance is compared between the methods...
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