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Provides warnings if tagged parameters do not match code, parsed parameters included in XML output and Doxygen-style tagfile (-D flag in 8.7). Partial C preprocessor support with -p flag. Support for #if/#ifdef control over documentation inclusion using the -D and -U command-line flags. Imagix 4D: customizable through style sheets and CSS
Sphinx converts reStructuredText files into HTML websites and other formats including PDF, EPub, Texinfo and man.. reStructuredText is extensible, and Sphinx exploits its extensible nature through a number of extensions – for autogenerating documentation from source code, writing mathematical notation or highlighting source code, etc.
The site was created in 2010 by Eric Holscher, Bobby Grace, and Charles Leifer. [4]On March 9, 2011, the Python Software Foundation Board awarded a grant of US$840 to the Read the Docs project for one year of hosting fees. [5]
View PDFs in a variety of zoom layouts, annotate, tag, search, cross-reference. Utopia Documents: GNU GPL v3: Semantic scientific PDF reader (optimized for life sciences and medicine), allows public comments on PDFs, generates on-the-fly link-outs to scientific databases and resources when used while online. Xpdf: Proprietary, freeware
Conceptual documentation consists of topics written using a MAML document type schema such as how to, walk-through, troubleshooting and several others. Sandcastle provides a conceptual build component stack (conceptual.config) that resolves shared content and links, and uses XSL files to transform MAML elements into HTML.
reStructuredText (RST, ReST, or reST) is a file format for textual data used primarily in the Python programming language community for technical documentation.. It is part of the Docutils project of the Python Doc-SIG (Documentation Special Interest Group), aimed at creating a set of tools for Python similar to Javadoc for Java or Plain Old Documentation (POD) for Perl.
Document retrieval is defined as the matching of some stated user query against a set of free-text records. These records could be any type of mainly unstructured text , such as newspaper articles , real estate records or paragraphs in a manual.
The global interpretation assumes that there exist some fixed set of underlying topics derived from inter-document similarity. These global clusters or their representatives can then be used to relate relevance of two documents (e.g. two documents in the same cluster should both be relevant to the same request).