A unifying similarity measure for automated identification of national implementations of european union directives

This paper presents a unifying text similarity measure (USM) for automated identication of national implementations of European Union (EU) directives. The proposed model retrieves the transposed provisions of national law at a ne-grained level for each article of the directive. USM incorporates methods for matching common words, common sequences of words and approximate string matching. It was used for identifying transpositions on a multilingual corpus of four directives and their corresponding national implementing measures (NIMs) in three dierent languages: English, French and Italian. We further utilized a corpus of four additional directives and their corresponding NIMs in English language for a thorough test of the USM approach. We evaluated the model by comparing our results with a gold standard consisting of ocial correlation tables (where available) or correspondences manually identied by domain experts. Our results indicate that USM was able to identify transpositions with average F-score values of 0.808, 0.736 and 0.708 for French, Italian and English Directive-NIM pairs respectively in the multilingual corpus. A comparison with state-of-the-art methods for text similarity illustrates that USM achieves a higher F-score and recall across both the corpora.