governmental, scientific, and private data. Because they have been standardized and are widely used, many reliable, efficient, and convenient tools for processing data in these formats are readily available. For instance, your favorite programming language undoubtedly has libraries for parsing XML and HTML as well as reading and transforming images in JPEG or movies in MPEG. Query engines are available for querying XML documents. Widely-used applications like Microsoft Word and Excel automatically translate documents between HTML and other standard formats. In short, life is good when working with standard data formats. In an ideal world, all data would be in such formats. In reality, however, we are not nearly so fortunate. An ad hoc data format is any non-standard data format. Typically, such formats do not have parsing, querying, analysis, or transformation tools readily available. Every day, network administrators, financial analysts, computer scientists, biologists, chemists, astronomers, and physicists deal with ad hoc data in a myriad of complex formats. Figure 1 gives a partial sense of the range and pervasiveness of such data. Since off-the-shelf tools for processing these ad hoc data formats do not exist or are not readily available, talented scientists, data analysts, and programmers must waste their time on low-level chores like parsing and format translation to extract the valuable information they need from their data.

An ad hoc data format is any non-standard, semi-structured data format for which robust data processing tools are not available. In this paper, we present ANNE, a new kind of mark-up language designed to help users generate documentation and data processing tools for ad hoc text data. More specifically, given a new ad hoc data source, an ANNE programmer will edit the document to add a number of simple annotations, which serve to specify its syntactic structure. Annotations include elements that specify constants, optional data, alternatives, enumerations, sequences, tabular data, and recursive patterns. The ANNE system uses a combination of user annotations and the raw data itself to extract a context-free grammar from the document. This context-free grammar can then be used to parse the data and transform it into an XML parse tree, which may be viewed through a browser for analysis or debugging purposes. In addition, the ANNE system will generate a PADS/ML description [21], which may be saved as lasting documentation of the data format or compiled into a host of useful data processing tools ranging from parsers, printers and traversal libraries to format translators and query engines. Overall, ANNE simplifies the process of generating descriptions for data formats and improves the productivity of programmers who work with ad hoc data regularly. In addition to designing and implementing ANNE, we have devised a semantic theory for the core elements of the language. This semantic theory describes the editing process, which translates a raw, unannotated text document into an annotated document, and the grammar extraction process, which generates a context-free grammar from an annotated document. We also present an alternative characterization of system behavior by drawing upon ideas from the field of relevance logic. This secondary characterization, which we call relevance analysis, specifies a direct relationship between unannotated documents and the context-free grammars that our system can generate from them. Relevance analysis allows us to prove a number of important theorems concerning the expressiveness and utility of our system. 1.