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This paper proposes an innovative graph-based text summarization model for generic single and multi-document summarization. The approach involves four unique processing stages: parsing sentences semantically using Semantic Role Labeling (SRL), grouping semantic arguments while matching semantic roles to Wikipedia concepts, constructing a weighted semantic graph for each document and linking its sentences...
Considering today's surge of information, the need for well organized knowledge bases is increasing rapidly for providing simplified access to knowledge and its further processing. In biomedical domain, heaps of information is buried in scientific publications and online forums. This calls for representing this information in a more expressive semantic way by determining and storing relational information...
This paper describes the construction of an intelligent semantics-based system that exploits several knowledge bases to tell contextually relevant stories to individuals and groups. Starting from information stored in user profiles, textual queries and pictures, a set of readily available tools recognize topics of interest and features of context, thereupon, we run data mining and semantic reasoning...
In this paper we propose a probabilistic topic model that incorporates DBpedia knowledge into the topic model for tagging Web pages and online documents with topics discovered in them. Our method is based on integration of the DBpedia hierarchical category network with statistical topic models where DBpedia categories are considered as topics. We have conducted extensive experiments on two different...
Steganography is helping individual to send confidential data between two parties. It enables user to hide data in different digital mediums. Steganography is of many types such as image steganography, text steganography, audio/video steganography etc. Text Steganography is quite difficult than other techniques because of less amount of redundancy and changes can be detected quite easily. Some of...
Extraction and integration of entities from textual data and linking them to knowledgebases (for further information or processing) is useful for many applications in natural language processing. However, a major problem in this process is disambiguation, named entities might refer to different things. In this work, we propose a novel method to disambiguate named entities. Our method is a combination...
Disadvantages of modern mass communication online tools are discussed in this paper. A new model for online forum is proposed to eliminate these disadvantages. Algorithms for the model are proposed. Methods for implementation of these algorithms are reviewed. The software architecture implementing the proposed model is described in this work.
In the article, relevance of system development of subject search of electronic educational resources on the Internet using computational linguistics is proved. The basic principles of system functioning are defined. The way of query view is considered. The practical problem of semantic optimization of inquiry consisting in creation of templates of collocations in the context of the considered discipline...
Application-defined and location-independent addressing is a founding principle of information centric networking (ICN) that is inherently difficult to realize if one also wants scalable routing and forwarding. We propose an ICN architecture, called TagNet, intended to combine expressive application-defined addressing with scalable routing and forwarding. TagNet features two independent delivery services:...
The exponential development in online social media allows users around the globe the possibility to share and communicate information and ideas freely in different formats of data via internet. This emerging media has become a dominant communication tool and it has been used as a communication channel in several events, especially “The Arab Spring” and BOSTON'S attack etc. In order to develop useful...
Today internet has become easily accessible which allows the user to perform multiple tasks such as access information, do study, make friends, online shopping, search for anything they want and many more. Similarly people do use internet to know the better options and find out relevant alternatives of product, services, places like wise. But searching for better choices may become frustrating and...
This paper investigates the problem of modeling Internet images and associated text for cross-modal retrieval tasks such as text-to-image search, and image-to-text search. Canonical correlation analysis (CCA), a classic two view approach for mapping text and image into a common latent space, does not make use of the semantic information of text and image pairs. We use CCA to map text, image and semantic...
Chinese food names are important language resources, which can be used in analysis of food reviews. Since the naming of Chinese food names are quite flexible and food reviews are typical spoken language, it is not easy to construct a general list for them. In this paper, we propose an approach to extracting Chinese food names from a large unlabeled Chinese corpus. At first, we construct character-level...
This paper presents a case study of discovering and classifying verbs in large web-corpora. Many tasks in natural language processing require corpora containing billions of words, and with such volumes of data co-occurrence extraction becomes one of the performance bottlenecks in the Vector Space Models of computational linguistics. We propose a co-occurrence extraction kernel based on ternary trees...
Machine-learning state-of-the-art keyphrase extraction systems do not take into consideration the fact that part of these keyphrases may not be found in the text. Therefore these systems typically use a training set restricted to textual terms, reducing the learning capabilities of any inductive algorithm. Our research investigates ways to improve the accuracy of these systems by allowing classification...
The extraction of semantic contexts is a relevant issue in information retrieval to provide high quality query results. This paper introduces the semantic context underlying a set of given input concepts as defined by the relevant multiple explanation paths connecting the input concepts in a collaborative network. A pheromone-like model based on this approach is introduced for the detection and the...
Most of the current text understanding techniques are based on ontology engine and external knowledge resources to reach to a deep comprehension. In this paper, we propose a computerized text comprehension technique for a given text. This technique can accommodate a deep text comprehension by an iterative reading of reference texts related to the given text using ontology engine. Performance analysis...
Enormous efforts of human volunteers have made Wikipedia become a treasure of textual knowledge. Relation extraction that aims at extracting structured knowledge in the unstructured texts in Wikipedia is an appealing but quite challenging problem because it's hard for machines to understand plain texts. Existing methods are not effective enough because they understand relation types in textual level...
The paper presents Semantic Concept Analysis (SCA) framework intended for automatic data-driven design of actionable ontology specifying mobile device user's personal interest's hierarchy together with dual structure reflecting the user's preferences over these interests. The framework integrates known technique for semi-automatic ontology design exploiting DBpedia and Wikipedia categories, on the...
Information retrieval - finding and retrieving relevant sources of data, such as documents or geospatially located records - is a bottleneck in the process of accessing online data. Metadata describing data sources is variable in quality and quantity, textual descriptions are defined by data providers and the terminology they use will not always match search terms, particularly in fields with specialised...
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