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of text summarization is accurate identification of keywords from the given textual content. In this paper, the relative performance of three popular algorithms, namely TextRank, LexRank and Latent Semantic Analysis for keyword extraction were investigated by measuring their effectiveness in identifying keywords from
of vocabulary words in the users speech utterance. In this paper, we investigate an approach that can be deployed in keyword spotting systems. We propose a phoneme classifier that will be ultimately used to provide confidence values to be compared against existing Automatic Speech Recognizer word confidences. The end
classic statistical method for sentence alignment, we propose an improved approach to align the initial bilingual resources, in which two factors, bilingual keyword pairs and matching patterns are introduced. Experimental results show that our sentence aligner supported by the new approach achieves performance enhancement by
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