The identification of cognates is a fundamental process in historical linguistics, on which any further research is based. Even though there are several cognate databases for Romance languages, they are rather scattered, incomplete, noisy, contain unreliable information, or have uncertain availability. In this paper we introduce a comprehensive database of Romance cognates and borrowings based on the etymological information provided by the dictionaries. We extract pairs of cognates between any two Romance languages by parsing electronic dictionaries of Romanian, Italian, Spanish, Portuguese and French. Based on this resource, we propose a strong benchmark for the automatic detection of cognates, by applying machine learning and deep learning based methods on any two pairs of Romance languages. We find that automatic identification of cognates is possible with accuracy averaging around 94% for the more difficult task formulations.

Semantic divergence in related languages is a key concern of historical linguistics. Intra-lingual semantic shift has been previously studied in computational linguistics, but this can only provide a limited picture of the evolution of word meanings, which often develop in a multilingual environment. In this paper we investigate semantic change across languages by measuring the semantic distance of cognate words in multiple languages. By comparing current meanings of cognates in different languages, we hope to uncover information about their previous meanings, and about how they diverged in their respective languages from their common original etymon. We further study the properties of their semantic divergence, by analyzing how the features of words such as frequency and polysemy are related to the divergence in their meaning, and thus make the first steps towards formulating laws of cross-lingual semantic change.

The review and selection process for scientific paper publication is essential for the quality of scholarly publications in a scientific field. The double-blind review system, which enforces author anonymity during the review period, is widely used by prestigious conferences and journals to ensure the integrity of this process. Although the notion of anonymity in the double-blind review has been questioned before, the availability of full text paper collections brings new opportunities for exploring the question: Is the double-blind review process really double-blind? We study this question on the ACL and EMNLP paper collections and present an analysis on how well deep learning techniques can infer the authors of a paper. Specifically, we explore Convolutional Neural Networks trained on various aspects of a paper, e.g., content, style features, and references, to understand the extent to which we can infer the authors of a paper and what aspects contribute the most. Our results show that the authors of a paper can be inferred with accuracy as high as 87% on ACL and 78% on EMNLP for the top 100 most prolific authors.