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XML employs a tree-structured model for representing data, and queries over XML documents are typically represented as twig patterns. At the same time, keyword search over XML documents has been well studied because of its intuitive and friendly query interface. Consequently, XQuery Full-Text emerges as a full-text
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
developer's development context to provide design pattern usage examples from projects that have a similar functional domain to that of the developer. In our approach, the Latent Dirichlet Allocation model is used to extract domain keywords from individual projects' source code. The domain keywords are stored in a fact
Most of the current focused crawling approaches perform syntactic matching, that is, they retrieve documents that contain particular keywords from the user's query. This often leads to poor discovery results, because the keywords in the query can be semantically similar but syntactically different, or vice-versa
trigger keywords and contextual cues. The system was tested on multiple large collections of Dutch tweets. Our experimental results show that our system can successfully analyze messages and recognize threatening content.
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