| Abstract |
This thesis concentrates on Data Mining in Corpus Linguistic. We show the use of modern Data Mining by developing efficient and effective methods for research and teaching in Corpus Linguistics in the fields of lexicography and semantics. Modern language resources as they are provided by Common Language Resources and Technology Infrastructure (http://clarin.eu) offer a large number of heterogeneous information resources of written language. Besides large text corpora, additional information about the sources or publication date of the documents from the corpora are available. Further, information about words from dictionaries or WordNets offer prior information of the word distributions. Starting with pre-studies in lexicography and semantics with large text corpora, we investigate the use of latent variable methods to extract hidden concepts in large text collections. We show that these hidden concepts correspond to meanings of words and subjects in text collections. This motivates an investigation of latent variable methods for large corpora to support linguistic research
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