Argumentation mining aims to automatically detect, classify and structure argumentation in text. Therefore, argumentation mining is an important part of a complete argumentation analyisis, i.e. understanding the content of serial arguments, their linguistic structure, the relationship between the preceding and following arguments, recognizing the underlying conceptual beliefs, and understanding within the comprehensive coherence of the specific topic. We present different methods to aid argumentation mining, starting with plain argumentation detection and moving forward to a more structural analysis of the detected (...) argumentation. Different state-of-the-art techniques on machine learning and context free grammars are applied to solve the challenges of argumentation mining. We also highlight fundamental questions found during our research and analyse different issues for future research on argumentation mining. (shrink)

Planning problems arise in law when an individual (or corporation)wants to perform a sequence of actions that raises legal issues. Manylawyers make their living planning transactions, and a system thathelped them to solve these problems would be in demand.The designer of such a system in a common-law domain must addressseveral difficult issues, including the open-textured nature of legal rules,the relationship between legal rules and cases, the adversarial nature ofthe domain, and the role of argument. In addition, the system's design isconstrained (...) by the fact that the intended users are lawyers, and its operation and output must be convenient for lawyers to use.In this article, I describe a system called CHIRON that I have developed to explore solutions to these issues. This system develops simple plans from representations of statutes and cases in the domain of United States personal income tax planning. (shrink)

This paper discusses some engineering considerations that should be taken into account when building a knowledge based system, and recommends isomorphism, the well defined correspondence of the knowledge base to the source texts, as a basic principle of system construction in the legal domain. Isomorphism, as it has been used in the field of legal knowledge based systems, is characterised and the benefits which stem from its use are described. Some objections to and limitations of the approach are discussed. The (...) paper concludes with a case study giving a detailed example of the use of the isomorphic approach in a particular application. (shrink)

Patent landscaping is the process of finding patents related to a particular topic. It is important for companies, investors, governments, and academics seeking to gauge innovation and assess risk. However, there is no broadly recognized best approach to landscaping. Frequently, patent landscaping is a bespoke human-driven process that relies heavily on complex queries over bibliographic patent databases. In this paper, we present Automated Patent Landscaping, an approach that jointly leverages human domain expertise, heuristics based on patent metadata, and machine learning (...) to generate high-quality patent landscapes with minimal effort. In particular, this paper describes a flexible automated methodology to construct a patent landscape for a topic based on an initial seed set of patents. This approach takes human-selected seed patents that are representative of a topic, such as operating systems, and uses structure inherent in patent data such as references and class codes to “expand” the seed set to a set of “probably-related” patents and anti-seed “probably-unrelated” patents. The expanded set of patents is then pruned with a semi-supervised machine learning model trained on seed and anti-seed patents. This removes patents from the expanded set that are unrelated to the topic and ensures a comprehensive and accurate landscape. (shrink)