Abstract
A growing amount of information is stored in relational databases and many scenarios involving human-computer interaction by means of natural language can be distilled to the problem of designing interfaces to relational databases that are driven by natural language. The talk presents approaches to human-computer interaction by means of natural speech or free text. The methods described focus on relational domains — such as Air Travel Information Systems (ATIS) — where semantic models are well defined by simple entity-relationship diagrams (schemas). We distinguish between techniques that aim at classifying a speech utterance or typed sentence into some category (call/text routing) and higher resolution forms of information extraction from text or speech that aim at recovering more precise domain-specific semantic entities such as dates, city/airport names, airlines, etc. The first part of the talk will focus on simple speech utterance/text classification techniques such as n-gram, Na?ve Bayes, and Maximum Entropy. The second part outlines an attempt at using the structured language model (SLM) — as a syntactic parser enriched with semantic tags — for extracting fine-grained semantic information from text.

Biography
Ciprian Chelba graduated from the Center for Language and Speech Processing at the Johns Hopkins University in January 2000. After graduation he joined the Speech Technology Group at Microsoft Research (http://research.microsoft.com~chelba). His core research interests are in statistical language and speech processing while the broader ones could be loosely described as statistical modeling. When not producing floating point numbers and trying to make sense out of them he goes out and enjoys outdoors activities such as hiking, tennis and skiing as well as a good play or movie.