Your success as a scientist will in part be measured by the quality of your research publications in high-quality journals and conference proceedings. Of the three classical rhetorical techniques, it is logos, rather than pathos or ethos, which is most commonly associated with scientific publications. In the mathematical sciences the paradigm for publication is to describe the mathematical proofs of propositions in sufficient detail to allow duplication by interested readers. Quality control is achieved by a system of peer review commonly referred to as refereeing. This guide is an attempt to distill the experience of the theoretical computer science community on the subject of refereeing into a convenient form which can be easily distributed to students and other inexperienced referees. Although aimed primarily at theoretical computer scientists, it contains advice which maybe relevant to other mathematical sciences. It may also be of some use to new authors who are unfamiliar with the peer review process. However, it must be understood that this is not a guide on how to write papers. Authors who are interested in improving their writing skills can consult the "Further Reading" section. The main part of this guide is divided into nine sections. The first section describes the

Students are expected to have reading knowledge of PASCAL or C, and to be able to follow explanations of recursive algorithms.

Knowledge is essential for understanding discourse. Generally, this has to be common sense knowledge and therefore, discourse understanding is hard. For the understanding of textbook proofs, however, only a limited quantity of knowledge is necessary. In addition, we have gained something very essential: inference. A prerequisite for parsing textbook proofs is to being able to parse formulae that occur in these proofs. Parsing formulae alone in the empty context is trivial. But within the context of textbook proofs the task soon gets complex. Several kinds of references from the text to parts or sets of terms and formulae have to be handled. We describe some of the linguistic phenomena that occur in mathematical texts. The focus is on our treatment of term reference which is embedded in the DRT.

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. Our long-range goal is to implement a program for the machine verification of textbook proofs. We study the task from both the linguistics and deduction perspective and give an in-depth analysis for a sample textbook proof. A three phase model for proof understanding is developed: parsing, structuring and refining. It shows that the combined application of techniques from both NLP and AR is quite successful. Moreover, it allows to uncover interesting insights that might initiate progress in both AI disciplines. Keywords: automated reasoning, natural language processing, discourse analysis 1 Introduction In [12], John McCarthy notes that "Checking mathematical proofs is potentially one of the most interesting and useful applications of automatic computers". In the first half of the 1960s, one of his students, namely Paul Abrahams, implemented a Lisp program for the machine verification of mathematical proofs [1]. The program, named Proofchecker, "was primarily directed towar...

We discuss the kind of writing that's appropriate in a paper submitted to the math department to complete Phase Two of MIT's writing requirement. First, we review the general purpose of the requirement and the specific way of completing it for the math department.