Novel similarity-based clustering algorithm for grouping broadcast news

The goal of the current paper is to introduce a novel clustering algorithm that has been designed for grouping transcribed textual documents obtained out of audio, video segments. Since audio transcripts are normally highly erroneous documents, one of the major challenges at the text processing stage is to reduce the negative impacts of errors gained at the speech recognition stage. Other difficulties come from the nature of conversational speech. In the paper we describe the main difficulties of the spoken documents and suggest an approach restricting their negative effects. In our paper we also present a clustering algorithm that groups transcripts on the base of informative closeness of documents. To carry out such partitioning we give an intuitive definition of “informative field of a transcript” and use it in our algorithm. To assess informative closeness of the transcripts, we apply Chi-square similarity measure, which is also described in the paper. Our experiments with Chi-square similarity measure showed its robustness and high efficacy. In particular, the performance analysis that have been carried out in regard to Chi-square and three other similarity measures such as Cosine, Dice, and Jaccard showed that Chi-square is more robust to specific features of spoken documents.