@PhdThesis{Brants:1999,
AUTHOR = {Brants, Thorsten},
TITLE = {Tagging and Parsing with Cascaded Markov Models - Automation of Corpus Annotation. Saarbrücken Dissertations in Computational Linguistics and Language Technology, Volume 6.},
YEAR = {1999},
ADDRESS = {Saarbrücken},
SCHOOL = {Universität des Saarlandes},
URL = {http://www.dfki.de/lt/diss/diss_en.htm},
ABSTRACT = {This thesis presents new techniques for parsing natural language. They are based on Markov Models, which are commonly used in part-of-speech tagging for sequential processing on the word level. We show that Markov Models can be successfully applied to other levels of syntactic processing. First, two classification tasks are handled: the assignment of grammatical functions and the labeling of non-terminal nodes. Then, Markov Models are used to recognize hierarchical syntactic structures. Each layer of a structure is represented by a separate Markov Model. The output of a lower layer is passed as input to a higher layer, hence the name: Cascaded Markov Models. Instead of simple symbols, the states emit partial context-free structures. The new techniques are applied to corpus annotation and partial parsing and are evaluated using corpora of different languages and domains.}
}
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