Technical documents with complex structures and orthography present special difficulties for current parsing technology. These include technical notation such as subscripts, superscripts and numeric and algebraic expressions as well as Greek letters, italics, small capitals, brackets and punctuation marks. Structural elements such as references to figures, tables and bibliographic items also cause problems. We first hand-code documents in Standard Generalized Markup Language (SGML) to specify the document’s logical structure (paragraphs, sentences, etc.) and capture significant orthography. Next, a regular expression analyzer produced by LEX is used to tokenize the SGML text. Then a token-based phrasal lexicon is used to identify the longest token sequences in the input that represent single lexical items. This lookup is efficient because limits on lookahead are precomputed for every item. After this, the Alvey Tools parser with specialized subgrammars is used to discover items such as floating-point numbers. The product of these preprocessing stages is a text that is acceptable to a full natural language parser. This work is directed towards automating the building of knowledge bases from research articles in the field of bacterial chemotaxis, but the techniques should be of wide applicability.