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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
enable people with specific demands, e.g., handicapped or elderly, to live in their preferred environment longer. The term “Internet of Things” (IoT) is used as an umbrella keyword for covering various aspects related to the extension of the Internet and the Web into the physical realm, by means of the
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
Retrieving Proper Names (PNs) specific to an audio document can be useful for vocabulary selection and OOV recovery in speech recognition, as well as in keyword spotting and audio indexing tasks. We propose methods to infer and retrieve OOV PNs relevant to an audio news document by using probabilistic topic models
This study introduces an example-based chat-oriented dialogue system with personalization framework using long-term memory. Previous representative chat-bots use simple keyword and pattern matching methodologies. To maintain the quality of systems, generating numerous heuristic rules with human labour is inevitable
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
efforts when studying a bug report, the proposed prototype also provides an extractive summary visualization of each bug report. In this research, it is shown that our proposed prototype performs better in terms of precision, recall, and F-measure than a baseline approach that uses time-sensitive keyword extraction.
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
problems that the developers search solutions for. The frequent switching between web browser and the IDE is both time-consuming and distracting, and the keyword-based traditional web search often does not help much in problem solving. In this paper, we propose an Eclipse IDE-based web search solution that exploits the APIs
holistic VLE framework, catering to the Malaysian education landscape. In order to conduct the meta-analysis and keyword mapping of these frameworks, Qualitative Data Analysis is used where empirical, methodological controlled analysis of texts within their context of communication are coded, deciphered and analyzed. The
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
propose to make out the best from the vital knowledge present over the internet intelligently, anywhere in the world through better searching methodologies. The current information retrieval system uses the keyword matching technique for fetching results from the web-repository. In this paper, we propose a structural
a card game playable with either single or multiple participants. The game consists of seventy cards, which have a keyword and a picture on one side and a hypothetical situation typically encountered in software development landscapes with two different selectable options on the other side. The game master (GM) reads a
demonstrate our automatically built fragment dictionary is capable of good localization. Finally, our fragment dictionary supports a keyword based fragment search system, which allows artists to get the fragments they need to make image collages.
lattice is introduced and used to guess the cause. Two cases of interactive and iterative process are shown where a user proceeds from a simple query to a new hypothesis, which would not be able to found by a naive cross tabulation or keyword extraction.
As the popularity of text-based source code analysis grows, the use of stemmers to strip suffixes has increased. Stemmers have been used to more accurately determine relevance between a keyword query and methods in source code for search, exploration, and bug localization. In this paper, we investigate which
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