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This paper presents a system architecture that designs a querying refinement method. The method employs the general principles of facet analysis in a particular paradigm, as well as the notion of ‘focus’, which is a sort of context for a user query. The method provides the user with contextual information about the query, which is computed by using the user documents provided that the documents are...
Distributed process execution is a scalable solution for implementing workflows that deal with many devices in the Internet of Things (IoT). In hierarchical network structures the execution of activities and subprocesses can be moved closer to the computing edge onto specialized devices while still preserving data security. However mobility, resource constraints and varying connectivity of devices...
In post disaster situation, the existing network infrastructure might be partly or fully damaged. In that case, a very popular online social network like twitter can be an effective tool, where people can share their views and knowledge about what is actually happening in the affected areas. It is a very challenging task to analyze the situation during the golden hours of any large scale disaster...
The question-answering systems were being investigated for several decades, but the majority of researches were carried out in English. The subject of this paper is the knowledge-based question-answering system. The unique mathematical model describes the process of answering when the question is presented in Russian as a natural language. The model is executed by mapping the question to the existing...
The ever increasing need to provide a suitable derivative of a term governed by context where derivative can either stemmed word or hypernym of a word has spurred a lot of research activities in information retrieval communities. In this paper, we are concerned with providing context centric derivatives of a term which can be useful in any search engine for obtaining better search results. Personalized...
In addition to identifying safety deficiencies evidenced by accidents, the independent investigative body makes recommendations to eliminate or reduce these deficiencies. Its main purpose is to advance transportation safety by conducting investigations of accidents in rail and other modes of transportation. He must answer several questions: what happened, why did it happen, and what can be done to...
Automatic and spontaneous speech emotion recognition is an important part of a human-computer interactive system. However, emotion identification in spontaneous speech is difficult because most often the emotion expressed by the speaker are not necessarily as prominent as in acted speech. In this paper, we propose a spontaneous speech emotion recognition framework that makes use of the associated...
The current work blends the different paradigms of Question Answering systems and presents a content-aware hybrid architecture for an open-domain factoid questions. It combines a knowledge-based, information extraction-based and a web-based approach in a pipelined architecture to construct an answer to a question keeping the context and discourse of the question in view. The proposed semantic-aware...
Lifelong learning models are popularly used with big data analysis as it learns better with the volume and variety of data. The model learns independently through an augmented learning mechanism that does not require manual support. Learning wrong and irrelevant rules are expected as it follows an unsupervised approach and therefore, the model is supported with a filtering mechanism. The rules that...
Measuring the similarity between strings plays an increasingly important role in many applications such as information retrieval, short answer grading, and conversational agent software. There has been much recent research interest in applying string similarity within Arabic language applications; however, the use of string similarity in Arabic poses a substantial challenge such as the complexity...
This paper puts forward a knowledge push method which is based on multidimensional hierarchical context model for general business process, and constructs a multi-dimensional hierarchical model of business process and a context driven knowledge resource database model, which emphasizes the mapping relation between knowledge and the knowledge context. On this basis, a framework of the knowledge push...
Huge volume of content is produced on multiple online sources every day. It is not possible for a user to go through these articles and read about topics of interest. Secondly professional articles, blog and forum have many topics discussed in a single discussion. Automatic knowledge-based topic models is a recent approach in Natural Language Processing that extract high quality topics from a large...
Word Sense Disambiguation (WSD) methods based on supervised learning usually convert WSD to a classification problem. Traditional WSD methods based on supervised learning often only consider the word, position, part of speech and some other superficial morphological and syntactic features. However, for a certain kind of polysemous words, their different senses usually appear in characteristic contexts...
The main goal of this paper is to explain important terms of the word sense disambiguation (WSD) in the Slovak language. A comprehensive survey of current approaches and evaluation methodologies is provided. Special attention is given to necessary language resources and tools. The paper deals with problems specific to Slovak language: missing language resources, rich morphology, free word order and...
Personalized financial advisory systems based on Case-Based Reasoning and on historical user activity are an emerging trend. In the present paper, we report the experience related to the development of a case-based recommendation module in a project called SMARTFASI, where the knowledge about past experiences is exploited, in order to suggest suitable asset investments to the final user. We present...
In this paper a methodology for disambiguating the word senses of polysemous words using Lexical Categories present in WordNet is presented. WordNet is a commonly used English lexical database. The algorithm is applied to the data scraped from Wikipedia articles. The representative context used in the algorithm is extracted from the Wikipedia pages of the words belonging to the category. The lexical...
Describes Tutorial I: "Context-Awareness in Information Retrieval and Recommender Systems" (Presented by Yong Zheng) and Tutorial II: "Ontology Learning and Population from Text Background" (Presented by Rosario Girardi).
In recent years Semantic Web technologies and the Linked Data paradigm have allowed the emergence of large interlinked knowledge bases as Linked datasets. These databases contain information that associates Web entities (called resources) with a well-defined semantics that specifies how these entities should be interpreted. A way to perform this task is through a class assignment process where resources...
Although the volume of online educational resources has dramatically increased in recent years, many of these resources are isolated and distributed in diverse websites and databases. This hinders the discovery and overall usage of online educational resources. By using linking between related subsections of online textbooks as a testbed, this paper explores multiple knowledge-based content linking...
Many classification tasks on short text, such as tweet, fail to achieve high accuracy due to data sparseness. One approach to solving this problem is to enrich the context of data by using external data sources, or distributed language representations trained on huge amount of data. In this paper, we present several tweet topic classification methods by exploiting different types of data: tweet text,...
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