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In order to solve the problem brought from the enormous policy texts and the complex management in the social insurance field, the article uses ontology as the way of representing and storing the knowledge. The article first constructed the framework of the ontology through manual work to ensure the relative accuracy of the ontology structure. Then it achieved the automatic ontology expansion based...
appearance characteristics, so called visual features. This paper proposes a method to cluster the scientific documents based on visual features, so called VF-Clustering algorithm. Five kinds of visual features of documents are de-fined, including body, abstract, subtitle, keyword and title. The thought of crossover and
To cope with the problem of information overload, various information filtering system have been proposed. However, most of them represent documents and user's interests as bag of words, and thus the intrinsic structures and semantic information in them are neglected. The information filtering mainly depended on the matching of simple key word or bag of words. In this paper, a multi-agent personalized...
In this paper, we studied a speaker independent isolated speaker recognition system for Turkish language by using cross correlation technique. The power spectrumpsilas of each keyword speech for different speakerpsilas determined using the linear predictive coding in order to constitute a feature vectors database that
Web services like conceptual search, i.e., search based on meaning rather than just character strings, has been the motivation of a large body of research in the IR field.
browsing can identify an image. In text-based retrieval, images are retrieved using keywords, like subject, headings, or classification codes, which in turn are used as retrieval keys during search and retrieval of images. Usually, the only way of searching these collections of images was by keyword indexing, or simply by
In this paper we present a spoken query detection method based on posteriorgrams generated from Deep Boltzmann Machines (DBMs). The proposed method can be deployed in both semi-supervised and unsupervised training scenarios. The DBM-based posteriorgrams were evaluated on a series of keyword spotting tasks using the
Users usually have different prospective even they input a same keyword to search Web services. It is a challenge to personalize web service search engine as more and more keyword-like Web services becoming available on Internet. User interest plays an important role in personalizing search result. Therefore, through
Traditional Web search engines do not use the images in the HTML pages to find relevant documents for a given query. Instead, they typically operate by computing a measure of agreement between the keywords provided by the user and only the text portion of each page. In this paper we study whether the content
using feature vector. We do static analysis over computed features to get distinguishing feature descriptors. Maximum similarity i.e. minimum distance allows us to find the query relevant combined pictures and associated relevant words. For textual part of the query we compute the concepts (keywords as well as synonyms of
are keyword-based and few imply the end-user during the search (through relevance feedback). Visual low-level descriptors are then substituted to keywords but there is a gap between visual description and user expectations. We propose a new framework which combines a multi-objective interactive genetic algorithm
Despite the tremendous importance and availability of large video collections, support for video retrieval is still rather limited and is mostly tailored to very concrete use cases and collections. In image retrieval, for instance, standard keyword search on the basis of manual annotations and content-based image
Co-clustering is a promising technique for summarizing cooccurrence information such as purchase history transactions and document-keyword frequencies. A close connection between fuzzy c-means (FCM) and Gaussian mixture models (GMMs) have been discussed and several extended FCM algorithms, which are induced by the
comprehensive and quality feeds for real-time event detection. In this paper, we present a novel adaptive keyword identification approach to retrieve a greater amount of event relevant content. This approach continuously monitors emerging hashtags and rates them by their similarity to specific pre-defined event hashtags using TF
-aware similarity method that uses a support vector machine and a domain dataset from a context-specific search engine query. Our filtering approach uses a spherical associated keyword space algorithm that projects filtering results from a three-dimensional sphere to a two-dimensional (2D) spherical surface for 2D
overloaded sites for a short piece of information of their interest. The crawler developed in the system gathers web page information which is processed using Natural Language Processing and Procedure programming for a specific keyword. The system returns precise short string answers or list to natural language questions
task of ad hoc information retrieval is, finding documents within a corpus like Bible, that are relevant to the user remains a hard challenge. Sometimes the relevant documents may not contain the specified keyword. The lack of the given term in a document does not necessarily mean that the document is not a relevant
components rather than a single Database table. So to minimise the time constraint, memory space and to do a smart search a new IR system is introduced. In the proposed system, searches can be divided into three categorise, namely (i) Main topic search (ii) Subtitle search and (iii) Keyword search. So the system would search
Users sometimes cannot remember the exact words of desktop filenames and meet trouble in re-finding these desktop files. In this condition the existing keyword based desktop search tools cannot work well. In this paper, we first analyze the synonym relationship among the words utilized in naming personal desktop files
Collaborative tagging systems have recently emerged as a powerful way to label and organize large collections of data. The informal social classification structure in these systems, also known as folksonomy, provides a convenient way to annotate resources by allowing users to use any keyword or tag that they find
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