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Access control models implement mechanisms to restrict access to sensitive data from unprivileged users. Access controls typically check privileges that capture the semantics of the operations they protect. Semantic smells and errors in access control models stem from privileges that are partially or totally unrelated to the action they protect. This paper presents a novel approach, partly based on...
The goal of the research described here is to present an approach for automating the detection and the extraction of meaning from text using a range of linguistic and ontological techniques, concepts such as the lexico-semantic functions proposed in Meaning-Text Theory by Mel'cuk and the concept of the context. This is motivated, by the fact that, on one hand, these functions enable a better modeling...
In this paper we describes our approach for automatic generation of learning objects' semantic metadata. The extraction process is based on the OBIE (Ontology Based Information Extraction) systems' principles. The input of our approach is a set of IEEE LOM metadata elements in conformance with two requirements. First, each data element must describe the educational content of the learning object....
In this paper we describes our approach for automatic generation of learning objects' semantic metadata. The extraction process is based on the OBIE (Ontology Based Information Extraction) systems' principles. The input of our approach is a set of IEEE LOM metadata elements in conformance with two requirements. First, each data element must describe the educational content of the learning object....
Topic detection is an hot research in the area of information retrieval. However, the new environment of Internet, the content of which are usually user-generated, asks for new requirements and brings new challenges. Topic detection has to resolve the problem of its lower quality and large amount of noisy. This paper not only provides a solution for detecting hot topics, but also giving its semantic...
This paper investigates the role of Distributional Semantic Models (DSMs) in Question Answering (QA), and specifically in a QA system called Question Cube. Question Cube is a framework for QA that combines several techniques to retrieve passages containing the exact answers for natural language questions. It exploits Information Retrieval models to seek candidate answers and Natural Language Processing...
Low information quality is one of the reasons why information extraction initiatives fail. Incomplete information has a pervasive negative impact on downstream processing steps. This work addresses this problem with a novel information extraction approach, which integrates data mining and information extraction methods into a single complementary approach in order to benefit from their respective...
In this paper, we propose a semantic query expansion approach by extending the query-regularized mixture model to include latent topics and apply it to spoken documents. We also propose to use context feature vectors for spoken segments to train SVM models to enhance the posterior-weighted normalized term frequencies in lattices. Experiments on Mandarin broadcast news showed that this approach offered...
Bug localization involves the use of information about a bug to assist in locating sections of code that must be modified to fix the bug. Such a task can involve a considerable amount of time and effort on the part of software developers and/or maintainers. Recently, several automated bug localization techniques based on information retrieval (IR) models have been developed to speed the process of...
Traditional information retrieval systems lack consistent semantic description of information i.e. they fail to meet users' need due to lack of applying semantic identification to extract the information from the available information. Use of semantic equivalent of the user query will improve the efficiency of the search. In this paper, we propose a framework for semantic based information retrieval...
In the past few years, there has been an explosive growth in scientific and legal information related to the patent system. Patents and related documents are siloed into multiple heterogeneous sources. Retrieving relevant information from diverse sources is a non-trivial task and poses many technical challenges. Among the challenges is the issue of terminological inconsistencies that are used in the...
Modern society is characterized by abundance of data, yet lack of (relevant) information. A major challenge consists in selecting valuable information according to specific criteria. Moreover, ranking it and defining relevance according to the context is decisive. We propose a framework to retrieve content according to context. Our approach relies on a three layers model, each contributing to a better...
Keyword based search scheme imposes the problem of representing a lot of web pages in the search engines. Query expansion with relevant words increases the performance of search engines, but finding and using the relevant words is an open problem. In this research we describe a new model for query expansion which employs user context and semantic concepts to discover new words for obtaining accurate...
Information Extraction using Natural Language Processing (NLP) produces entities along with some of the relationships that may exist among them. To be semantically useful, however, such discrete extractions must be put into context through some form of intelligent analysis. This paper offers a two-part architecture that employs the statistical methods of traditional NLP to extract discrete information...
With the rapid development of Internet, how to obtain and represent personalized information in user's activity of information seeking is a key issue in information retrieval. This paper presents a novel method to build personalized search context which represents semantic background in user's information seeking. Search context is composed of terms and semantic relation which is extracted based on...
Due to the importance of high-quality customer service, many companies use intelligent helpdesk systems (e.g., case-based systems) to improve customer service quality. However, these systems face two challenges: 1) Case retrieval measures: most case-based systems use traditional keyword-matching-based ranking schemes for case retrieval and have difficulty to capture the semantic meanings of cases...
Noun phrase understanding is very important for many sub-fields of natural language processing and information retrieval. This paper proposed a classification framework for Chinese post-modified V+N phrases. The basic idea is that most noun phrases might be mapped to corresponding clauses. Therefore, case, time, aspect and modality can also be encoded in noun phrases as in verb phrases. All those...
With the development of natural language processing (NLP) technology, the need for automatic named entity recognition (NER) is highlighted in order to enhance the performance of information extraction systems. In this paper, a hybrid model for Chinese person based on conditional random fields model is proposed, which fuses multiple features. It differentiates from most of the previous approaches,...
Social bookmarking tools are rapidly emerging on the Web as it can be witnessed by the overwhelming number of participants. In such spaces, users annotate resources by means of any keyword or tag that they find relevant, giving raise to lightweight conceptual structures aka folksonomies. In this respect, needless to mention that ontologies can be of benefit for enhancing information retrieval metrics...
Expansion of query keywords based on semantic relationship is an effective approach to improve the performance of text retrieval. In this paper, a novel approach for text retrieval is presented. The principle of the approach is to construct a integrated semantic tree, and select candidate keywords from the tree. On the tree, all nodes are weighted based on synonymy, hypernymy, and Mutual Information...
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