Target paper title: detecting code-switching in a multilingual alpine heritage corpus

Target paper abstract: This paper describes experiments in detecting and annotating code-switching in a large multilingual diachronic corpus of Swiss Alpine texts. The texts are in English, French, German, Italian, Romansh and Swiss German. Because of the multilingual authors (mountaineers, scientists) and the assumed multilingual readers, the texts contain numerous code-switching elements. When building and annotating the corpus, we faced issues of language identification on the sentence and subsentential level. We present our strategy for language identification and for the annotation of foreign language fragments within sentences. We report 78% precision on detecting a subset of code-switches with correct language labels and 92% unlabeled precision.

Source paper title: leveraging data-driven methods in word-level language identification for a multilingual alpine heritage corpus
Source paper title : This paper presents a data-driven, simple cluster-and-label approach using optimized count-based methods for word-level language identification for a large domain-specific multilingual diachronic corpus of periodicals published at least yearly between 1864 and 2014 in Switzerland. Our system requires no annotated data or training, only minimal human effort in evaluating and labeling 50 clusters for a corpus of almost 40 million tokens. Despite being unsupervised, our results show an accuracy that is comparable to the corpus annotations which result from an existing code switching algorithm and the combined usage of two supervised systems using character and byte n-gram models (NAME_1 and Clematide, 2014). Provide your response in exactly 5 paragraphs (separated nearby paragraphs with 3 aterisk symbols ***).
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