Abstract
This paper presents a prototype for the retrieval of Italian broadcast news, which has been developed at ITC-irst. The architecture employs a speech recognition engine for the automatic transcription of audio news. Moreover, it features document indexing based on part-of-speech tagging of text coupled with morphological analysis, and query expansion exploiting the Italian WordNet thesaurus. Query-document matching is based on a statistical term weighting scheme. The system was tested on a 203-story collection of audio news, augmented with 9500 newspaper articles. The evaluation was based on a “known item” retrieval task and aimed at evaluating the impact of speech recognition errors and query expansion on retrieval performance.
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