Editor`s note: We are pleased to publish this article by Qiang Lu and Jack Conrad, who both worked with Thomson Reuters R&D as part of the WestlawNext research team. Jack Conrad continues to work with Thomson Reuters, although he is currently on loan at Thomson Reuters Global Resources` Catalyst Lab in Switzerland. Qiang Lu is now based in Kore Federal in the Washington, DC area. We read with interest their paper from the 2012 International Conference on Knowledge Engineering and Ontology Development (KEOD), “Bringing order to legal documents: An issue-based recommendation system via cluster association”, and we are grateful that they agreed to provide system-specific context for their work in this area. Your current contribution is a practical description of progress. The ability to find relevant documents in large collections of documents is a fundamental element of legal research. The emergence of large collections of machine-readable legal documents has stimulated research aimed at improving the quality of the tools used to access these collections. Significant research has been conducted within the traditional communities of information retrieval, artificial intelligence and law, with varying degrees of interaction between these groups. This article provides an introduction to text search and provides an overview of the most important search results related to retrieving legal documents. Thomson Reuters legal editors are known for their specialized and authoritative resources that help lawyers succeed. Explore our list of legal book publishers to find the titles that are right for you. Studies have shown that some natural language processing (NLP) systems encode and reproduce harmful biases with potentially negative ethical implications in our society.
In this article, we propose an approach to identifying gender and racial stereotypes in the embeddings of words formed on legal opinions of American jurisprudence. Integrations that contain stereotypical information can cause harm when used by downstream systems for classification, information extraction, answering questions, or other machine learning systems used to create legal research tools. We first explain why the methods previously proposed to identify these biases are not well suited for use with embedded words formed on the text of legal notices. We then propose a field-appropriate method for identifying gender and racial bias in the legal field. Our analyses using these methods suggest that racial and gender biases are encoded in embeddings of words formed on legal opinions. These biases are not mitigated by the exclusion of historical data and occur in several broad areas of law. Implications for downstream systems using the integration of legal advice words and suggestions for possible mitigation strategies based on our observations will also be discussed. With recent advances in machine learning models, we have seen improvements in natural language inference (NLI) tasks, but the legal consequences have been difficult, especially for supervised approaches. This document describes a highly scalable, state-of-the-art dataset aggregation system and the basic infrastructure designed to support it.
The system, called PeopleMap, allows lawyers to search effectively and efficiently across a wide range of public records databases using a single person-centred search. The backbone support system, called Concord, is a toolkit that enables developers to cost-effectively create dataset resolution solutions. The PeopleMap system is capable of linking billions of public documents to a master data record consisting of hundreds of millions of personal records. It was created using successive Concord applications to link disparate records of public records to a central person authority file. To our knowledge, the PeopleMap system is the largest of them. Use integrated legal marketing solutions to attract, engage, and connect with potential clients. Our cutting-edge solutions range from lawyers` custom websites and search engine optimization to online advertising and the power of FindLaw.com leads. Learn about the products that legal, tax and business professionals rely on to perform at their best. The WIN recovery engine is West`s implementation of the inference network recovery model. The inference network model organizes documents into a coherent probabilistic framework based on the combination of different proofs, such as textual representations such as words, sentences, or paragraphs. WIN is based on the same recovery model as the INQUERY system used in previous TREC competitions. The two recovery engines have common roots, but have evolved separately – WIN has focused on retrieving legal material from large collections (>50 gigabytes) in an online business environment that supports Boolean language and natural language recovery.
For TREC-3, we decided to run a substantially unchanged version of WIN to see how a state-of-the-art commercial system compares. This article describes the techniques we used for the different tasks we participated in for the COLIEE 2021 competition. Canadian jurisprudence and the Japanese Civil Code had five tasks related to legal recuperation and challenges to involvement. We explain the methodology we applied to each task with the validation results. We use a variety of techniques ranging from simple metrics like TF-IDF word overlap to the most advanced integration models like BERT or GPT-3. Multi-label document classification has a wide range of applicability for various practical problems, such as labeling press articles, sentiment analysis, medical code classification, etc. Various approaches (e.g., tree methods, neural networks, and deep learning systems based specifically on pre-trained linguistic models) have been developed to address classification problems for multi-label documents and have performed well on different data sets. In the legal field, however, you often face several key challenges when working with multi-label classification tasks. A major challenge is the lack of high-quality human-labeled datasets that prevent researchers and practitioners from adequately performing their respective tasks. In addition, existing multi-label classification methods generally focus on majority classes, resulting in unsatisfactory performance for other large classes that do not have sufficient training samples. To address the above challenges, in this article we first present POSTURE50K, a new multi-label extreme legal classification dataset that we will make available to the research community.
The dataset contains 50,000 legal opinions and their manually labeled legal procedures. The labels in this dataset follow a zipfian distribution, so many classes have only a few samples. In addition, we offer a deep learning architecture that uses domain-specific pre-training and a label attention mechanism to classify documents with multiple labels. We evaluate our proposed architecture on POSTURE50K and another EUROLEX57K multi-label legal dataset and show that our approach works better than two core systems and four other current state-of-the-art methods for both datasets. Use legal books, ProView eBooks, and legal software to search court rules by industry and jurisdiction. This document discusses the two main strategies for evaluating queries in large text retrieval systems and analyzes the performance of these strategies.