Choosing the wrong word in a machine translation or natural language generation system can convey unwanted connotations, implications, or attitudes. The choice between near-synonyms such as "error", "mistake", "slip", and "blunder" - words that share the same core meaning, but differ in their nuances - can be made only if knowledge about their differences is available.

In this talk I present a method to automatically acquire a new type of lexical resource: a knowledge-base of near-synonym differences. I present in detail the unsupervized decision-lists algorithm that learns extraction patterns from a special dictionary of synonym differences. The patterns are then used to extract knowledge from the text of the dictionary.

The initial knowledge-base is later enriched with information from other machine-readable dictionaries. Information about the collocational behaviour of the near-synonyms is acquired from free text. I also briefly present Xenon, a natural language generation system that shows how the new lexical resource can be used to choose the best near-synonym in specific situations.