In the context of the Competition on Legal Information Extraction/Entailment (COLIEE), we propose a method comprising the necessary steps for finding relevant documents to a legal question and deciding on textual entailment evidence to provide a correct answer. The proposed method is based on the combination of several lexical and morphological characteristics, to build a language model and a set of features for Machine Learning algorithms. We provide a detailed study on the proposed method performance and failure cases, indicating that it is competitive with state-of-the-art approaches on Legal Information Retrieval and Question Answering, while not needing extensive training data nor depending on expert produced knowledge. The proposed method achieved significant results in the competition, indicating a substantial level of adequacy for the tasks addressed.
AILQA: Evaluating AI-Driven Legal Question Answering Systems for the Indian Legal System
Indian Institute of Technology（印度理工学院）；Symbiosis Law School（共生法学院）；Indian Institute of Science Education and Research（印度科学教育与研究学院）；University of Birmingham（伯明翰大学）
From Query to Counsel: Structured Reasoning with a Multi-Agent Framework and Dataset for Legal Consultation
Australian Artificial Intelligence Institute (AAII), University of Technology Sydney（悉尼科技大学澳大利亚人工智能研究所）；School of Computer Science, University of Birmingham（伯明翰大学计算机科学学院）
NyayaRAG: Realistic Legal Judgment Prediction with RAG under the Indian Common Law System
Indian Institute of Technology Kanpur（印度理工学院坎浦尔分校）；SRM Institute of Science and Technology（SRM科学与技术学院）；Symbiosis Law School Pune（苏比斯法学院浦那分校）；IISER Kolkata（印度科学研究所库勒卡布尔分校）；University of Birmingham, Dubai, United Arab Emirates（布里斯托尔大学迪拜分校）
Semi-Automatic Data Annotation, POS Tagging and Mildly Context-Sensitive Disambiguation: the eXtended Revised AraMorph (XRAM)
EPHE（法国高等研究实践学院）；Sapienza University of Rome（罗马大学）；Roma Tre University（罗马第三大学）
Tim Buckwalter的阿拉伯语词汇和形态资源AraMorph，扩展修订AraMorph（以下简称XRAM）的扩展，修订形式，它解决了一些弱点和不一致的原始模型，允许更广泛地覆盖现实世界的古典和当代（正式和非正式）阿拉伯语文本。基于先前的研究，XRAM增强包括（i）标记可选择的使用标记，（ii）概率轻度上下文敏感的POS标记，过滤，消歧和替代形态分析的排名，（iii）通过从现有词汇资源中提取词汇和形态信息来半自动增加词汇覆盖率。通过前端Python模块对XRAM进行的测试显示出了显著的成功水平。
SinhaLegal: A Benchmark Corpus for Information Extraction and Analysis in Sinhala Legislative Texts
School of Computing, Informatics Institute of Technology（计算学院，信息学院技术研究所）；Department of Computer Science & Engineering, University of Moratuwa（计算机科学与工程系，莫图瓦大学）
Analyzing and Encoding the Al-Mawrid Arabic-English Dictionary with the ISO Language Markup Framework and TEI Lex-0
Faculty of Information Technology and Computer Science, Sinai University（信息科技与计算机科学学院， Sinai大学）；Inria；ISO committee TC 37 (Language and terminology)（ISO术语委员会TC 37（语言和术语））
Loci Similes: A Benchmark for Extracting Intertextualities in Latin Literature
Department of Computer and Information Science, University of Konstanz（计算机与信息科学系，康斯坦茨大学）；Department of Latin Philology, University of Konstanz（拉丁语言学系，康斯坦茨大学）；Department of Archaeology, Classical Philology and Ancient Studies, University of Zurich（考古学、古典语言学与古代研究系，苏黎世大学）
