Legal documents tend to be large in size. In this paper, we provide an experiment with attention-based approaches complemented by certain document processing techniques for judgment prediction. For the prediction of explanation, we consider this as an extractive text summarization problem based on an output of (1) CNN with attention mechanism and (2) self-attention of language models. Our extensive experiments show that treating document endings at first results in a 2.1% improvement in judgment prediction across all the models. Additional content peeling from non-informative sentences allows an improvement of explanation prediction performance by 4% in the case of attention-based CNN models. The best submissions achieved 8’th and 3’rd ranks on judgment prediction (C1) and prediction with explanation (C2) tasks respectively among 11 participating teams. The results of our experiments are published

Named Entity Recognition (NER) is a subtask of Natural Language Processing (NLP) that involves identifying and categorizing named entities. The result annotation makes unstructured natural texts applicable for other NLP tasks, including information retrieval, question answering, and machine translation. NER is also essential in legal as an initial stage in extracting relevant entities. However, legal texts contain domain-specific named entities, such as applicants, defendants, courts, statutes, and articles. The latter makes standard named entity recognizers incompatible with legal documents. This paper proposes an approach combining multiple models’ results via a voting mechanism for unique entity identification in legal texts. This endeavor focuses on extracting legal named entities, and the specific assignment (task B) is to create a legal NER system for unique entity annotation in legal documents. The results of our experiments and system implementation are published in https://github.com/SuperEDG/Legal_Project.

News articles often convey attitudes between the mentioned subjects, which is essential for understanding the described situation. In this paper, we describe a new approach to distant supervision for extracting sentiment attitudes between named entities mentioned in texts. Two factors (pair-based and frame-based) were used to automatically label an extensive news collection, dubbed as RuAttitudes. The latter became a basis for adaptation and training convolutional architectures, including piecewise max pooling and full use of information across different sentences. The results show that models, trained with RuAttitudes, outperform ones that were trained with only supervised learning approach and achieve 13.4% increase in F1-score on RuSentRel collection.