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Word Sense Disambiguation (WSD) has become a popular method for solving the ambiguous meaning of the words in Information Retrieval (IR) field area. Under the Natural Language Processing (NLP) community, WSD has been described as the task which able to select the appropriate meaning among the ambiguous meanings to a given word. Among three approaches, supervised based, unsupervised based and knowledge...
In this paper, we propose a novel semi- supervised learning strategy to address human parsing. Existing human parsing datasets are relatively small due to the required tedious human labeling. We present a general, affordable and scalable solution, which harnesses the rich contexts in those easily available web videos to boost any existing human parser. First, we crawl a large number of unlabeled videos...
In contemporary companies unstructured knowledge is essential, mainly due to the possibility to obtain better flexibility and competitiveness of the organization. For example, on the basis of automatic analysis of the experts' opinions, the decision-makers are capable of taking decisions (for example decisions concerning investments). This paper presents issues related to developing and evaluating...
Several paradigms for high-level music descriptions have been proposed to develop effective system for browsing and retrieving musical content in large repositories. Such paradigms are based on either categorical or dimensional models. The interest in dimensional models has recently grown a great deal, as they define a semantic relation between concepts through graded descriptions. One problem that...
The structuring of large volumes of e-documents assumes the organization of text on several levels, namely paragraphs, sentences, phrases, words. Methods of lexical paradigms extraction using statistical analysis were developed long ago. In this paper we attempt to move from lexical correlatives to the list of synonyms on various levels of generalization on the basis of local and global contexts'...
In course of a breaking news event, such as natural calamity, political uproar etc., a massive crowd sourced data is generated over social media which makes social media platforms an important source of information in such scenarios. The value of the information being propagated via social media is being increasingly realised by the news organisations and the journalists. Better tools and methodologies...
Semantic relation extraction is an important part of information extraction, it has application value in the automatic question answering system, retrieval system, ontology learning, semantic web annotation, and many other areas. Pattern representation method is context pattern in previous semi-Supervised semantic relation extraction based on bootstrapping, but it did not consider the role of the...
Word Sense Disambiguation (WSD) is the process of selecting the correct sense for a word in a context. WSD has become a growing research area in the field of Natural Language Processing (NLP). Over the decades, lot of studies had been carried out to suggest different approaches for WSD process. A break-through in this field would have a significant impact on many relevant web-based applications, such...
This article aims at evaluating the interest of the Wicri network, a network of semantic wikis, both as a reservoir of curation rules allowing to enrich corpora metadata and as a tool for parameterizing and supporting of instructions for the creation of corpora exploration servers. Starting, from the analysis of a bibliographic corpus extracted from different documentary databases, the experiments...
Question Analysis is an important task in Question Answering Systems (QAS). It consists generally in identifying the semantic type of the question and extracting the main focus of the question. The goal is to better specify the required information by the question. In this context and as part of a framework aiming to implement an Arabic opinion QAS for political debates, this paper addresses the problem...
Some of the search tasks users perform on the Web aim at complementing the information they are currently reading in a Web page: they are ancillary search tasks. Currently, the standard way to support such ancillary searches follows an inside-out approach, which means that query results are shown in a new window/tab or as a replacement of the current page. We claim that such inside-out approach is...
Context-awareness is becoming an important foundation of adaptive mobile systems, however, techniques for discovering contextually relevant Web content and Smart Devices (i.e., Smart Resources) remain consigned to small-scale deployments. To address this limitation, this paper introduces Ambient Ocean, a Web search engine for context-aware Smart Resource discovery. Ocean provides scalable mechanisms...
In recent years, the development of legal ontologies has increased significantly with the diversity of their applications known as complicated due to the complexity of their domain. In the preliminary part of this paper, we introduce the major steps in the learning process, then we present some works interested in Arabic ontology learning. The rest of the paper serves to propose our approach for ontology...
Structured knowledge bases are an increasingly important way for storing and retrieving information. Within such knowledge bases, an important search task is finding similar entities based on one or more example entities. We present QBEES, a novel framework for defining entity similarity based only on structural features, so-called aspects, of the entities, that naturally model potential interest...
SemioTag is an approach towards tagging that utilizes the semiotic sign categories icon, index, and symbol as classification structures to be used by users during the annotation and search of images within social media-oriented repositories. We compared the influence of this approach on the tagging and querying behaviour of users, with respect to usability, efficiency, and user experience, between...
We present a method for the automatic classification of text documents into a dynamically defined set of topics of interest. The proposed approach requires only a domain ontology and a set of user-defined classification topics, specified as contexts in the ontology. Our method is based on measuring the semantic similarity of the thematic graph created from a text document and the ontology sub-graphs...
In this paper, we represent a dynamic context-dependent weighting method for vector space model. A meaning is relatively decided by a context dynamically. A vector space model, including latent semantic indexing (LSI), etc. relatively measures correlations of each target thing that represents in each vector. However, the vectors of each target thing in almost method of the vector space models are...
With the growing needs of dimension reduction for term selection and recommendation and the up to date trends in natural language processing modules integrated in existing architectures and multiple semantic web system such as search engine. The existence of multiples tokenization techniques of the same text represents a persistent problem in current semantic search engine practice and create a non-trivial...
Information Technology brought many applications of Information Retrieval as simple as possible through Web and other Digital Information Access Environment. There is a demand in building Applications of IR in Local languages, which allows common people with minimal knowledge in at least one language to avail the information services. Word mismatch is a common problem in IR System Applications. The...
The ineffectiveness of information retrieval systems is mostly caused by the inaccurate query formed by a few keywords that reflect actual user information need. One well known technique to overcome this limitation is Automatic Query Expansion (AQE), whereby the user's original query is improved by adding new features with a related meaning. It has long been accepted that capturing term associations...
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