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Register a new userMulti-granular Legal Topic Classification on Greek Legislation
تصنيف الموضوع القانوني متعدد الحبيبية للتشريع اليوناني
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في هذا العمل، ندرس مهمة تصنيف النصوص القانونية المكتوبة باللغة اليونانية. نقدم واجعلنا متاحا علنا مجموعة بيانات جديدة تستند إلى التشريعات اليونانية، والتي تتكون من أكثر من 47 ألف مسؤول، صنفت موارد التشريعات اليونانية المصنفة. نقوم بتجربة هذه البيانات وتقييم بطارية الأساليب المتقدمة والصفوفات المصنفة، تتراوح من تعلم الآلات التقليدية والطرق القائمة على RNN إلى الأساليب القائمة على المحولات الحديثة. نظرا لأن الهندسة المعمارية المتكررة مع Adminings Word الخاصة بالمجال توفر الأداء العام المحسن أثناء التنافس حتى إلى النماذج القائمة على المحولات. أخيرا، نظهر أن النماذج المتطورة متعددة اللغات والأنتغات التي تعتمد على المحولات التي تعمل على أعلى تصنيف من تصنيف الصفوصين، مما يجعلنا شكا من ضرورة تدريب نماذج تعلم نقل أحادية التحويل كقاعدة عامة. على حد علمنا، هذه هي المرة الأولى التي يتم فيها النظر في مهمة تصنيف النص القانوني اليوناني في مشروع بحث مفتوح، في حين أن اليونانية هي لغة مع موارد NLP محدودة للغاية بشكل عام.
In this work, we study the task of classifying legal texts written in the Greek language. We introduce and make publicly available a novel dataset based on Greek legislation, consisting of more than 47 thousand official, categorized Greek legislation resources. We experiment with this dataset and evaluate a battery of advanced methods and classifiers, ranging from traditional machine learning and RNN-based methods to state-of-the-art Transformer-based methods. We show that recurrent architectures with domain-specific word embeddings offer improved overall performance while being competitive even to transformer-based models. Finally, we show that cutting-edge multilingual and monolingual transformer-based models brawl on the top of the classifiers' ranking, making us question the necessity of training monolingual transfer learning models as a rule of thumb. To the best of our knowledge, this is the first time the task of Greek legal text classification is considered in an open research project, while also Greek is a language with very limited NLP resources in general.
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In this paper, we introduce the Greek version of the automatic annotation tool ERRANT (Bryant et al., 2017), which we named ELERRANT. ERRANT functions as a rule-based error type classifier and was used as the main evaluation tool of the systems parti
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Hierarchical multi-label text classification (HMTC) deals with the challenging task where an instance can be assigned to multiple hierarchically structured categories at the same time. The majority of prior studies either focus on reducing the HMTC t
ask into a flat multi-label problem ignoring the vertical category correlations or exploiting the dependencies across different hierarchical levels without considering the horizontal correlations among categories at the same level, which inevitably leads to fundamental information loss. In this paper, we propose a novel HMTC framework that considers both vertical and horizontal category correlations. Specifically, we first design a loosely coupled graph convolutional neural network as the representation extractor to obtain representations for words, documents, and, more importantly, level-wise representations for categories, which are not considered in previous works. Then, the learned category representations are adopted to capture the vertical dependencies among levels of category hierarchy and model the horizontal correlations. Finally, based on the document embeddings and category embeddings, we design a hybrid algorithm to predict the categories of the entire hierarchical structure. Extensive experiments conducted on real-world HMTC datasets validate the effectiveness of the proposed framework with significant improvements over the baselines.
We introduce MULTI-EURLEX, a new multilingual dataset for topic classification of legal documents. The dataset comprises 65k European Union (EU) laws, officially translated in 23 languages, annotated with multiple labels from the EUROVOC taxonomy. We
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Bidirectional Encoder Representations from Transformers (BERT) has achieved state-of-the-art performances on several text classification tasks, such as GLUE and sentiment analysis. Recent work in the legal domain started to use BERT on tasks, such as
legal judgement prediction and violation prediction. A common practise in using BERT is to fine-tune a pre-trained model on a target task and truncate the input texts to the size of the BERT input (e.g. at most 512 tokens). However, due to the unique characteristics of legal documents, it is not clear how to effectively adapt BERT in the legal domain. In this work, we investigate how to deal with long documents, and how is the importance of pre-training on documents from the same domain as the target task. We conduct experiments on the two recent datasets: ECHR Violation Dataset and the Overruling Task Dataset, which are multi-label and binary classification tasks, respectively. Importantly, on average the number of tokens in a document from the ECHR Violation Dataset is more than 1,600. While the documents in the Overruling Task Dataset are shorter (the maximum number of tokens is 204). We thoroughly compare several techniques for adapting BERT on long documents and compare different models pre-trained on the legal and other domains. Our experimental results show that we need to explicitly adapt BERT to handle long documents, as the truncation leads to less effective performance. We also found that pre-training on the documents that are similar to the target task would result in more effective performance on several scenario.
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