Tagging and Parsing with Cascaded Markov Models -- Automation of Corpus Annotation

Autor: Thorsten Brants

Herausgeber:

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.