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Dutch for Reading Knowledge
This first Dutch for Reading Knowledge book on the market promotes a high level of reading and translation competency by drawing from Dutch grammar, vocabulary and reading strategies, and providing many translation \"shortcuts\" and tips when tackling complex texts in Dutch. Aimed at students, researchers and scholars who need to learn how to read and translate modern Dutch texts for their academic research, this book focuses on those areas where the Netherlands plays or has played a leading and innovative role in the world. These areas include architecture, art history, design, the Dutch Golden Age, (post)colonialism, (im)migration, social legislation and water management. For all areas the authors combine profound knowledge of the field with great expertise in teaching Dutch language and culture. This book can be used for a Dutch for Reading Knowledge course or curriculum, and is also highly suitable for self study.
eBook
Microblog language identification: overcoming the limitations of short, unedited and idiomatic text
Multilingual posts can potentially affect the outcomes of content analysis on microblog platforms. To this end, language identification can provide a monolingual set of content for analysis. We find the unedited and idiomatic language of microblogs to be challenging for state-of-the-art language identification methods. To account for this, we identify five microblog characteristics that can help in language identification: the language profile of the blogger (blogger), the content of an attached hyperlink (link), the language profile of other users mentioned (mention) in the post, the language profile of a tag (tag), and the language of the original post (conversation), if the post we examine is a reply. Further, we present methods that combine these priors in a post-dependent and post-independent way. We present test results on 1,000 posts from five languages (Dutch, English, French, German, and Spanish), which show that our priors improve accuracy by 5 % over a domain specific baseline, and show that post-dependent combination of the priors achieves the best performance. When suitable training data does not exist, our methods still outperform a domain unspecific baseline. We conclude with an examination of the language distribution of a million tweets, along with temporal analysis, the usage of twitter features across languages, and a correlation study between classifications made and geo-location and language metadata fields.
Journal Article
Cross-language plagiarism detection
Cross-language plagiarism detection deals with the automatic identification and extraction of plagiarism in a multilingual setting. In this setting, a suspicious document is given, and the task is to retrieve all sections from the document that originate from a large, multilingual document collection. Our contributions in this field are as follows: (1) a comprehensive retrieval process for cross-language plagiarism detection is introduced, highlighting the differences to monolingual plagiarism detection, (2) state-of-the-art solutions for two important subtasks are reviewed, (3) retrieval models for the assessment of cross-language similarity are surveyed, and, (4) the three models CL-CNG, CL-ESA and CL-ASA are compared. Our evaluation is of realistic scale: it relies on 120,000 test documents which are selected from the corpora JRC-Acquis and Wikipedia, so that for each test document highly similar documents are available in all of the six languages English, German, Spanish, French, Dutch, and Polish. The models are employed in a series of ranking tasks, and more than 100 million similarities are computed with each model. The results of our evaluation indicate that CL-CNG, despite its simple approach, is the best choice to rank and compare texts across languages if they are syntactically related. CL-ESA almost matches the performance of CL-CNG, but on arbitrary pairs of languages. CL-ASA works best on \"exact\" translations but does not generalize well.
Journal Article