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It was often the case that the legal situation was presented in the introduction and that the legal analysis was in the middle of the report. However, the reports did not follow a universal format. Conclusions were often short and situated at the end of the report. Case law citations are used to support legal argumentation and are therefore referred to as a part of legal analysis. For that reason they were rarely found in introduction or conclusion. Annotator 1 created annotation guidelines high level task definition, descriptions and examples for each category, and analyses of a few difficult cases in several iterations and trained Annotator 2 on their use.
The annotators were expected to identify sentences that contained the legal principles and facts of the cited cases, based on the written guidelines. Sentences associated with cited cases that are neither principles or facts are annotated as neutral. The key points of the guidelines, which were 7 pages long, are summarised below.
The task of annotation focused on the identification of cited information within annotation areas that were defined as paragraphs having at least one citation. Citation instances had been manually annotated prior to the study. Given the discussion of the complexity of jurisprudential views of legal principles, we have taken an operationalised view, based on the analysis of a legal scholar and key linguistic indicators. A nine page annotation manual was produced to define each category and provide examples and counter examples for difficult cases.
The key points are summarised here. Use the pre-annotated citation instances to identify annotation areas—i. Direct quotes and lists were treated as a part of the same paragraph. Label each sentence in each annotation area as one of fact , principle or neither , following the annotation guidelines.
Annotator 1 proceeded to create a gold corpus of 50 reports which was used for training a machine classifier, as described next. The methodology used for machine annotation employed classification of the annotation units with a Naive Bayesian Multinomial Classifier based on a set of selected features described below. Cit—a feature which indicates whether there is a citation instance in the sentence.
The contract contained a general condition that in relation to any financial or other conditions either party could at any time before the condition was fulfilled or waived avoid the contract by giving notice. In addition, we used three other features that captured the length of the sentence number of words , its position in the text on a scale of 0—1 and whether or not there is a citation in the sentence boolean.
Modern Business Law by Jonathan Merritt | Waterstones
The other features were derived by means of a python script. The Naive Bayes Multinomial classifier was used for the classification task. Results are reported for tenfold cross-validation. The sentences in the dataset were randomly partitioned into 10 subsamples. In each fold one of the subsamples was used for testing after training on the remaining 9 subsamples. Results are reported over the 10 testing subsamples, which constitute the entire dataset. Feature selection reduced the number of features from to ; we report more on the selected features later in this section.
Unigrams: is, a, the, or, be, Mr, was, must, of, had, may, has, see, 0, it, , where, I, were, other, are, if, will, to, concerned, general, person, , an, Mrs, and, judgment, party, that, planning, principle, letter, company, one, If, circumstances, which, per, money, whether, Hawk, always, submissions, not, Charles, Arista, court, jurisdiction, forum, can, pictures, Akzo, Miss, ordinary, fund, man, S1, contained, , wholesalers, this. Dependency pairs: case-that, is-there, concern-was, concern-case, case-was, parties-the, condition-a, court-the, is-if, letter-the. Better results may be achieved if the annotation guidelines are redefined to be more specific about what constitutes a fact or principle, for instance, the fact class could be limited only to the sentences whose aim is to introduce facts.
Introducing further features to determine the provenance of principles could help with the confusions between principles and neutral. Such positive results suggest that the methodology employed as a part of this experiment can provide a suitable basis for further work. Lawyers use case law citation to refer to existing legal principles. This practice stems from the doctrine of stare decisis that prescribes for the cases that are similar on facts to be treated in a similar way. There is no established formal methodology prescribing how legal principles should be extracted from the case or which facts should be treated as material for making the decision.
Citations are a valuable source of existing interpretations of case law which can be used to illustrate the legal principles that are supported by the cited case and material facts that are necessary for the principles to be invoked. This information is important for legal researchers, because it allows identifying a pool of relevant case law that can be used to build the argument. Automated analysis of legal principles and facts within cited cases allows identifying the key information about the cited case which can be used for many purposes, including creation of detailed case treatment summaries, improvement of search and retrieval methodology for the case law and many others.
This work demonstrates the feasibility of automatic identification of legal principles and facts that are associated with a case citation.
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This functionality could, for example, allow a legal practitioner to not only search, say in Google, for citations mentioned in a case, but also the associated legal principles and facts, providing deep access to and insight into the development of the law. It would also offer the opportunity to access the law directly rather than via the edited and structured materials made available by legal service providers. Finally, we have only addressed accessing cited legal principles and facts, which is distinct from ranking and relating precedents, i.
In future work, the source material annotated here could be used to investigate the automation of Shepardisation as well. GATE 8. Skip to main content Skip to sections. Advertisement Hide. Download PDF. Recognizing cited facts and principles in legal judgements.
Open Access. First Online: 11 March Before we describe our approach to citation analysis, certain key concepts of legal theory must be scoped, as this work is focused on the computational analysis of the language of the law rather than on legal theory.
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In particular, cases are considered to contain ratio decidendi , which can be translated as a reason for a decision , an important piece of reasoning that is incorporated into the argumentation structure of future decisions. A variety of approaches to defining ratio decidendi can be identified in legal theory.
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Greenawalt provides several explanations on what forms the binding part of a decision: 1 the rule s of law that the court explicitly states, or that can reasonably be inferred, that it regarded as necessary to or important in its resolution of the case [ In order to build a successful argument lawyers need to identify applicable law which includes common law authorities. Legal principles that exist within the common law are formulated and encoded in existing decisions, so for lawyers it is often necessary to identify a set of factually similar cases which can be cited to support the argument.
The research of legal authorities is often performed with the help of a number of tools, which according to Geist can be split into three categories—primary, secondary and tertiary: Primary sources are case law and legislation records that are created and distributed by a number of agents. Secondly, the complexity of interpretation and variance in conceptualisation can also make processing of citation information more challenging.
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Ogden a mentions that Greenleaf, the first publisher of a paper legal citator, found the following Marmor observes that overall there is not always sufficient agreement between the legal professionals regarding the case law The observations made by Marmor are in agreement with the conclusion recently reached by Greenawalt A legal principle is defined as any statement which is used, along with facts, to reach a conclusion. Linguistically, a legal principle can, for instance, be indicated by deontic modality, e. Legal principles can be qualified, e.
In contrast to legal principles, there are facts , which are statements bearing on what uncontroversially exists, occurred, or is a piece of information. For our purposes, only sentences that refer to events which occur outside the court hearing are annotated; this excludes procedural facts. Linguistically, facts present themselves with non-modal expressions and denoting expressions, e.
Following a period of training, a set of 10 reports were randomly selected all previously unseen by the annotators as the aforementioned control corpus for the inter-annotator and intra-annotation agreement studies reported here. The process in short was to: 1. It shows that Annotator 2, who does not have legal training, is more conservative in identifying facts and inferences than Annotator 1, who has had legal training.
Table 1 Distribution of categories. The following features were selected for extraction from the dataset: Part of speech tags. Dependency pairs. Length of the sentence. Position in the text. Part of speech tags were selected as a feature for a number of reasons. Firstly, it was expected that modal verbs and verb tense may help to classify the annotation units.
Sentences that introduce facts are most often presented in the Past tense. For example: The contract contained a general condition that in relation to any financial or other conditions either party could at any time before the condition was fulfilled or waived avoid the contract by giving notice.
Given the limited amount of labeled data available, there was no developmental stage employed. We instead used linguistic features that we expected to be useful, relied on automatic feature selection to prune the feature set, ran a single machine learning algorithm with default settings and report results using a cross-validation methodology, as detailed below: 1. Feature counts were normalised by tf and idf. The accuracy of the classifier is slightly better than that of the Annotator 2 as reported in Sect. The majority of classification errors involve confusion with the neither category.