Information Extraction from Broadcast News Speech Data

In this paper we describe a robust algorithm for information extraction from spoken language data. Our probabilistic algorithm builds on results in language modeling, using classbased smoothing to produce state-of-the-art performance for a wide range of speech error rates. We show that our system performs well with sparse data, as well as with out-of-domain data.