We consider the problem of checking whether an organization conforms to a body of regulation. Conformance is studied in a runtime verification setting. The regulation is translated to a logic, from which we synthesize monitors. The monitors are evaluated as the state of an organization evolves over time, raising an alarm if a violation is detected. An important challenge to this approach comes from the fact that regulations are commonly expressed in natural language. The translation to logic is difficult. Our goal is to assist in this translation by: (a) the design of logics that let us formalize regulation one sentence at a time, and (b) the use of natural language processing as an aid in the sentential translation. There are many features that are needed in a logic, to accommodate a sentential translation of regulation. We study two features, motivated by a case study. First, statements in regulation refer to others for conditions or exceptions. Second, sentences in regulation convey legal concepts, e.g., obligation and permission. Obligations and permissions can be nested to convey concepts, such as, rights. We motivate and design a logic to accomodate these two features of regulatory texts. The common theme is the importance of the notion of {\em saying} in such constructs. We begin by extending linear temporal logic to allow statements to refer to others. Inter-sentential references are expressed via the use of a predicate, called "says", whose interpretation is determined by inferences from laws. The "says" predicate offers a unified analysis of various kinds of inter-sentential references, e.g., priorities of exceptions over rules, and references to definitions or list items. We then augment the logic with obligation and permission, by considering problems in access control and conformance. Saying and permission are combined using an axiom that permits a principal to speak on behalf of another. The combination yields benefits to both applications. For access control, we overcome the problematic interactions between hand-off and classical reasoning. For conformance, we obtain a characterization of legal power by nesting saying with obligation and permission. A useful fragment of the logic has a polynomial time decision procedure. Finally, we turn to the use of natural language processing to translate a sentence to logic. We study one component of the translation in a supervised learning setting. Linguistic theories have argued for a level of logical form as a prelude to translating a sentence into logic. Logical form encodes a resolution of scope ambiguties. We define a restricted kind of logical form, called abstract syntax trees (ASTs), based on the logic developed. Guidelines for annotating ASTs are formulated, using a case study of the Food and Drug Administration's Code of Federal Regulations. We describe experiments on a modest-sized corpus, of about 200 sentences, annotated with ASTs. The main step in computing ASTs is the ordering or ranking of operators. We adapt a learning model for ranking to order operators. Features are designed by studying subproblems, such as, disambiguating between de re and de dicto interpretations. We obtain an F-score of 90.6% on the set of pairwise ordering decisions.