Abstract

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. 1. INTRODUCTION Extracting linguistic structure such as proper names, noun phrases, and verb phrases is an important first step in many systems aimed at automatic language understanding. While significant progress has been made on this problem, most of the work has focused on "clean" textual data such as newswire texts, where cues such as capitalization and punctuation are important for obtaining high accuracy results. However, there are many data sources where these cues are no t reliable, such as in spoken language data or single-case text. Spoken language sources, in particular, pose additional problems because of disflu...

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