A unifying similarity measure for automated identification of national implementations of european union directives
| Autor: | Daniel Traykov, Hristo Konstantinov, Francesco Costamagna, Llio Humphreys, Rohan Nanda, Livio Robaldo, Luigi Di Caro, Hristo Hristov, Guido Boella, Michele Romano, Tenyo Tyankov |
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| Přispěvatelé: | Nanda, Rohan, Robaldo, Livio, Romano, Michele, Di Caro, Luigi, Boella, Guido, Konstantinov, Hristo, Tyankov, Tenyo, Traykov, Daniel, Hristov, Hristo, Costamagna, Francesco, Humphreys, Llio |
| Rok vydání: | 2017 |
| Předmět: |
Artificial intelligence
European law Legal information retrieval Artificial intelligence Matching (statistics) Computer science 02 engineering and technology Similarity measure 0603 philosophy ethics and religion computer.software_genre Legal information retrieval 020204 information systems Similarity (psychology) 0202 electrical engineering electronic engineering information engineering media_common.cataloged_instance European union media_common Computer science [C05] [Engineering computing & technology] business.industry 06 humanities and the arts Approximate string matching Sciences informatiques [C05] [Ingénierie informatique & technologie] 16. Peace & justice Directive European law Identification (information) 060301 applied ethics Data mining business computer Natural language processing semantic similarity information retireval European law |
| Zdroj: | Proceedings of the 16th edition of the International Conference on Articial Intelligence and Law-ICAIL 17 Proceedings of the 16th edition of the International Conference on Articial Intelligence and Law -ICAIL '17 ICAIL A Unifying Similarity Measure for Automated Identification of National Implementations of European Union Directives. (2017). STARTPAGE=149;ENDPAGE=158;TITLE=None Proceedings of the 16th edition of the International Conference on Articial Intelligence and Law |
| DOI: | 10.1145/3086512.3086527 |
| Popis: | This paper presents a unifying text similarity measure (USM) for automated identification of national implementations of European Union (EU) directives. The proposed model retrieves the transposed provisions of national law at a fine-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 different 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 official correlation tables (where available) or correspondences manually identified 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. |
| Databáze: | OpenAIRE |
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