Legislation and regulations are expressed in natural language. Machine-readable forms of the texts may be represented as linked documents, semantically tagged text, or translation to a logic. The paper considers the latter form, which is key to testing consistency of laws, drawing inferences, and providing explanations relative to input. To translate laws to a machine-readable logic, sentences must be parsed and semantically translated. Manual translation is time and labour intensive, usually involving narrowly scoping the rules. While automated translation systems have made significant progress, problems remain. The paper outlines systems to automatically translate legislative clauses to a semantic representation, highlighting key problems and proposing some tasks to address them.
Semantic Annotations for Legal Text Processing using GATE Teamware
Adam Wyner and Wim Peters
Large corpora of legal texts are increasing available in the public domain. To make them amenable for automated text processing, various sorts of annotations must be added. We consider semantic annotations bearing on the content of the texts – legal rules, case factors, and case decision elements. Adding annotations and developing gold standard corpora (to verify rule-based or machine learning algorithms) is costly in terms of time, expertise, and cost. To make the processes efficient, we propose several instances of GATE’s Teamware to support annotation tasks for legal rules, case factors, and case decision elements. We engage annotation volunteers (law school students and legal professionals). The reports on the tasks are to be presented at the workshop.
By Adam Wyner
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.