this paper, we have presented a system for Tagging and Chunking based on an Integrated Language Model that uses a homogeneous formalism (finite-state machine) to cornbine different knowledge sources. It is feasible both in terms of performance and also in terms of computational efficiency

This paper presents a machine learning approach to shallow parsing using techniques of grammatical inference. We first learn a deterministic probabilistic automaton that models the joint distribution of chunk and Part-of-speech tags, and then use this automaton as a transducer to find the most likely chunk tag sequence using a dynamic programming algorithm. The resulting transducers can also be combined with statistical P05' taggers. We also discuss an efficient means of incorporating lexical information together with an application of bagging that improve our results.

Over the last few years, stochastic models have been widely used in the natural language understanding modeling. Almost all of these works are based on the denition of segments of words as basic semantic units for the stochastic semantic models. In this work, we present a two{level stochastic model approach to the construction of the natural language understanding component of a dialog system in the domain of database queries. This approach will treat this problem in a way similar to the stochastic approach for the detection of syntactic structures (Shallow Parsing or Chunking) in natural language sentences; however, in this case, stochastic semantic language models are based on the detection of some semantic units from the user turns of the dialog. We give the results of the application of this approach to the construction of the understanding component of a dialog system, which answers queries about a railway timetable in Spanish. 1