16,216 research outputs found
Recognizing cited facts and principles in legal judgements
In common law jurisdictions, legal professionals cite facts and legal principles from precedent cases to support their arguments before the court for their intended outcome in a current case. This practice stems from the doctrine of stare decisis, where cases that have similar facts should receive similar decisions with respect to the principles. It is essential for legal professionals to identify such facts and principles in precedent cases, though this is a highly time intensive task. In this paper, we present studies that demonstrate that human annotators can achieve reasonable agreement on which sentences in legal judgements contain cited facts and principles (respectively, κ=0.65 and κ=0.95 for inter- and intra-annotator agreement). We further demonstrate that it is feasible to automatically annotate sentences containing such legal facts and principles in a supervised machine learning framework based on linguistic features, reporting per category precision and recall figures of between 0.79 and 0.89 for classifying sentences in legal judgements as cited facts, principles or neither using a Bayesian classifier, with an overall κ of 0.72 with the human-annotated gold standard
Natural language processing
Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems
Legal Judgement Prediction for UK Courts
Legal Judgement Prediction (LJP) is the task of automatically predicting the outcome of a court case given only the case document. During the last five years researchers have successfully attempted this task for the supreme courts of three jurisdictions: the European Union, France, and China. Motivation includes the many real world applications including: a prediction system that can be used at the judgement drafting stage, and the identification of the most important words and phrases within a judgement. The aim of our research was to build, for the first time, an LJP model for UK court cases. This required the creation of a labelled data set of UK court judgements and the subsequent application of machine learning models. We evaluated different feature representations and different algorithms. Our best performing model achieved: 69.05% accuracy and 69.02 F1 score. We demonstrate that LJP is a promising area of further research for UK courts by achieving high model performance and the ability to easily extract useful features
CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap
After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in
multimedia search engines, we have identified and analyzed gaps within European research effort during our second year.
In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio-
economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown
of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on
requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the
community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our
Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as
National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core
technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research
challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal
challenges
CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines
Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective.
The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines.
From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research
Exploring the State of the Art in Legal QA Systems
Answering questions related to the legal domain is a complex task, primarily
due to the intricate nature and diverse range of legal document systems.
Providing an accurate answer to a legal query typically necessitates
specialized knowledge in the relevant domain, which makes this task all the
more challenging, even for human experts. QA (Question answering systems) are
designed to generate answers to questions asked in human languages. They use
natural language processing to understand questions and search through
information to find relevant answers. QA has various practical applications,
including customer service, education, research, and cross-lingual
communication. However, they face challenges such as improving natural language
understanding and handling complex and ambiguous questions. Answering questions
related to the legal domain is a complex task, primarily due to the intricate
nature and diverse range of legal document systems. Providing an accurate
answer to a legal query typically necessitates specialized knowledge in the
relevant domain, which makes this task all the more challenging, even for human
experts. At this time, there is a lack of surveys that discuss legal question
answering. To address this problem, we provide a comprehensive survey that
reviews 14 benchmark datasets for question-answering in the legal field as well
as presents a comprehensive review of the state-of-the-art Legal Question
Answering deep learning models. We cover the different architectures and
techniques used in these studies and the performance and limitations of these
models. Moreover, we have established a public GitHub repository where we
regularly upload the most recent articles, open data, and source code. The
repository is available at:
\url{https://github.com/abdoelsayed2016/Legal-Question-Answering-Review}
Enhanced services for targeted information retrieval by event extraction and data mining
Where Information Retrieval (IR) and Text Categorization delivers a set of (ranked) documents according to a query, users of large document collections would rather like to receive answers. Question-answering from text has already been the goal of the Message Understanding Conferences. Since then, the task of text understanding has been reduced to several more tractable tasks, most prominently Named Entity Recognition (NER) and Relation Extraction. Now, pieces can be put together to form enhanced services added on an IR system. In this paper, we present a framework which combines standard IR with machine learning and (pre-)processing for NER in order to extract events from a large document collection. Some questions can already be answered by particular events. Other questions require an analysis of a set of events. Hence, the extracted events become input to another machine learning process which delivers the final output to the user's question. Our case study is the public collection of minutes of plenary sessions of the German parliament and of petitions to the German parliament. --
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