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This paper proposes a method for keyword spotting in offline Chinese handwritten documents using a statistical model. On a text query word, the method measures the similarity between the query word and every candidate word in the document by combining a character classifier and four classifiers characterizing the
One of the most serious problems that conventional knowledge management (KM) encompasses has been pointed out tardy and ineffective acquisition of knowledge. To resolve this problem, knowledge must be autonomously acquired according to its context of use by applying the technique of keyword extraction in machine
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
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.
combined for a table top exercise to explore the effectiveness of using OSINT combined with a context aware handheld situational awareness framework and application to better inform potential responders as the events unfolded. Our experience shows that the ability to model sentiment, trends, and monitor keywords in streaming
. Simultaneously, it generates a personal context-aware dictionary dynamically from the keywords gotten via some APIs in the Internet. Currently, the information of user's context is also provided by NGN. In this paper, we explain the overview of our proposal and prototype implementation in Japanese.
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