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The study of traditional text filtering with keywords retrieval ignores the semantic relations between keywords, and it results in the bottleneck of the further development of text filtering. To solve this problem, three steps are adopted. Firstly, concept lattice theory should be introduced into traditional text
livelihoods, how to deal with its negative impacts, and which mitigation or adaptation policies to support. A line of related work has used bag of words and word-level features to detect frames automatically in text. Such works face limitations since standard keyword based features may not generalize well to accommodate surface
In this paper, an intelligent concept based search engine has been presented that can be used as a multilingual platform for different search queries. It retrieves those results pages also which don't have directly the keywords but contains the synonyms or related words. In response to a query for the word “car
Semantic search promises to provide more accurate result than present-day keyword matching-based search by using the knowledge represented logically (i.e., knowledge base). But, the ordinary users don't know well the complex formal query language and schema of the knowledge base. So, the system should interpret the
engine like Solr and Elastic Search to perform boolean search effectively. This problem is a vital cog in the wheel of text analytics world. It can also be extended to improvise the result of keyword extraction, abstractive summarization, and POS parser tree.
(MWE) and they do not scale very well. This paper proposes a clustering and classification algorithm for semantic similarity using sample web pages. Further improvement is to analyze the short text for classification and labeling the short text according to the keyword and producing the result for the end user. This type
(MWE) and they do not scale very well. This paper proposes a clustering and classification algorithm for semantic similarity using sample web pages. Further improvement is to analyze the short text for classification and labeling the short text according to the keyword and producing the result for the end user. This type
Criminal Intelligence Analysis often requires a search different from the semantic and keyword based searching to reveal the associations among semantically and operationally connected objects within a crime knowledge base. In this paper we introduce associative search as a search along the networks of association
to external hierarchical resource to polish accuracy of text matching. Also, a whole framework of text processing, keyword extraction and information matching is applied firstly among Chinese SMEs complementarity identification. By using machine learning algorithm, complementarities are digitalized and potential
in an electronic health record (EHR) system, keyword search within the chart may produce many results that are not relevant or that may overlook related expressions and concepts entirely. In addition, some medical events, such as the occurrence of symptoms, are associated with important attributes such as location or
systems have been proposed. Although these systems have proved to be more effective in processing candidate resumes and matching them to their relevant job posts, they still suffer from low precision due to limitations of their underlying techniques. On the one hand, approaches based on keyword matching ignore the semantics
The World Wide Web contains vast amount of interlinked web documents. Retrieving information from such a huge collection is easy using various search engines, but retrieving relevant information is still a challenging task. Since the traditional search engines are based upon keyword matching, therefore semantics of
page next to the keyword that motivated the user to launch an ancillary search. In order to demonstrate the feasibility of our approach we have developed a tool that embeds an egocentric information visualization technique in the Web page. This tool supports nested queries and allows the display of multiple data
recommendation approaches that support the Requirements Engineering (RE) process. First, we propose a Keyword Recommender to increase requirements reuse. Second, we define a thesaurus enhanced Dependency Recommender to help stakeholders finding complete and conflict-free requirements. Finally, we present studies conducted at the
-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
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
Common search engines, especially web-based, rely on standard keyword-based queries and matching algorithms using word frequencies, topics recentness, documents authority and/or thesauri. However, even if those systems present efficient retrieval algorithms, they are not able to lead the user into an intuitive
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
A Max-Probability Density based Clustering (MPDC) algorithm is proposed in this paper to resolve the problem of Word Sense Disambiguation in semantic document. MPDC take the context information of a keyword based on WordNet into account and select the max probability sense by measuring the density of the concept. We
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
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