Several recent efforts in statistical nat- urM language understanding (NLU) have focused on generating clumps of English words from semantic meaning concepts (Miller et al., 1995; Levin and Pieraccini, 1995; Epstein et al., 1996; Epstein, 1996). This paper extends the IBM Ma- chine Translation Group's concept of fertil- ity (Brown et al., 1993) to the generation of clumps for natural language understanding. The basic underlying intuition is that a single concept may be expressed in English as many disjoint clump of words. We present two fertility models which attempt to capture this phenomenon. The first is a Poisson model which leads to appealing computational simplicity. The second is a general nonparametric fertility model. The general model's parameters are bootstrapped from the Poisson model and updated by the EM algorithm. These fertility models can be used to impose clump fertility structure on top of preexisting clump generation models. Here, we present resuits for adding fertility structure to unigram, bigram, and headword clump generation models on ARPA's Air Travel Infor- mation Service (ATIS) domain.

d my ignorance in the field. He was always patient, and took the time to explain his answers at a level I could understand. iv Dr. Todd Ward, a colleague of mine at IBM, has also "been there" for me. I cannot count the number of times that Todd helped me figure out a solution to a problem, either mathematical or programming. Whenever I was not sure about a solution to a problem, Todd was my sounding board. I'm sure that his individual research efforts were slowed by our meetings, but that never stopped him from helping me. Todd also acted as a counselor, providing insight on how to complete a doctorate! Former IBMer, Dr. Stephen Della Pietra, is without a doubt the brightest mathematician with whom I have ever worked. Like Salim and Todd, he knows statistical modeling at a much greater depth than I do, and he never minded "bringing down" the level of his explanations to one where I could understand and absorb the material. Stephen was my mentor, and without his expert tutelag

The development of natural language interfaces (NLIs) for databases has been an interesting problem in natural language processing since the 70's. The need for NLIs has become more pronounced given the widespread access to complex databases now available through the Internet. However, such systems are difficult to build and must be tailored to each application. A current research topic involves using machine learning methods to automate the development of NLI's. This proposal presents a method for learning semantic parsers (systems for mapping natural language to logical form) that integrates logic-based and probabilistic methods in order to exploit the complementary strengths of these competing approaches. More precisely, an inductive logic programming (ILP) method, TABULATE, is developed for learning multiple models that are integrated via linear weighted combination to produce probabilistic models for statistical semantic parsing. Initial experimental results from three d...

We present a natural language interface system which is based entirely on trained statistical models. The system consists of three stages of processing: parsing, semantic interpretation, and discourse. Each of these stages is modeled as a statistical process. The models are fully integrated, resulting in an end-to-end system that maps input utterances into meaning representation frames.