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 9,500 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
A System for the Retrieval of Italian Broadcast News
Federico, Marcello
2000-01-01
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 9,500 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 performanceI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
