Authorship Attribution with Support Vector Machines

In this paper we explore the use of text-mining methods for the identification of the author of a text. We apply the support vector machine (SVM) to this problem, as it is able to cope with half a million of inputs it requires no feature selection and can process the frequency vector of all words of a text. We performed a number of experiments with texts from a German newspaper. With nearly perfect reliability the SVM was able to reject other authors and detected the target author in 60–80% of the cases. In a second experiment, we ignored nouns, verbs and adjectives and replaced them by grammatical tags and bigrams. This resulted in slightly reduced performance. Author detection with SVMs on full word forms was remarkably robust even if the author wrote about different topics.

An examination of the relationship between social locations of Chinese immigrants and their ethnic newspaper dependency

This study drew upon media system dependency theory (MSD) and social identity theory to examine the relationship between social locations of Chinese immigrants and their dependency on Chinese ethnic newspapers. Data was obtained from a survey participated by 265 respondents with Chinese origin but currently residing in Australia. Results indicated that among the three indicators of social location, age appeared to be a strong positive predictor of the dependency on ethnic newspapers for information. Respondents who stayed longer in the host country tended to be more frequent readers of ethnic newspapers as well. Education did not appear as a significant predictor of ethnic newspaper dependency. These findings suggested the need for us to further investigate the impact of ethnic print media on ethnic minorities in the age of various information sources offered by new technologies.

Ontologies, taxonomies, thesauri:learning from texts

The use of ontologies as representations of knowledge is widespread but their construction, until recently, has been entirely manual. We argue in this paper for the use of text corpora and automated natural language processing methods for the construction of ontologies. We delineate the challenges and present criteria for the selection of appropriate methods. We distinguish three ma jor steps in ontology building: associating terms, constructing hierarchies and labelling relations. A number of methods are presented for these purposes but we conclude that the issue of data-sparsity still is a ma jor challenge. We argue for the use of resources external tot he domain specific corpus.