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Keyword spotting is the task of detecting keywords of interest in continuous speech. This work investigates the application of keyword spotting to detect crime and it can be used along with telephone tapping and audio monitoring devices by security organization. In this work phonetic based word spotter is developed. A
The problem of automatically extracting the most interesting and relevant keyword phrases in a document has been studied extensively as it is crucial for a number of applications. These applications include contextual advertising, automatic text summarization, and user-centric entity detection systems. All these
In cross-language information retrieval (CLIR), the query sentence is often combined with a series of query keywords, rather than a complete natural sentence. Lack of necessary contextual syntactic information in such a query sentence makes it impossible to achieve a unique translation of the query sentence with
keyword specified by the investigator or suggested by system. Experiments were conducted on dummy crime dataset to test the accuracy and the scalability of the proposed system. Experimental results proved that subject suggestion improved the accuracy and thus speeds up the process of searching the evidence.
verification in software development process. We developed a dictionary tool to support the translation from natural language to formal language. The tool provides functionalities those are easy registration of keywords to the dictionary and exhaustive marking of the keywords. The dictionary represents a map between equivalent
Traditional information retrieval (IR) systems evaluate user queries and retrieve/rank documents based on matching keywords in user queries with words in documents.These exact word-matching and ranking approaches ignore too many relevant documents that do not contain the exact keywords as specified in a user query
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