 Finally, statistical analysis investigates word meaning by examining through computational means the distribution of words in linguistic corpora. The main idea is to use quantitative data about the frequency of co-occurrence of sets of lexical items to identify their semantic properties and differentiate their different senses (for overviews, see Atkins &amp; Zampolli 1994; Manning &amp; Schütze 1999; Stubbs 2002; Sinclair 2004). Notice that while symbolic networks are models of the architecture of the lexicon that seek to be psychologically adequate (i.e., to reveal how knowledge of word meaning is stored and organized in the mind/brain of human speakers), statistical approaches to word meaning are not necessarily interested in psychological adequacy, and may have completely different goals, such as building a machine translation service able to mimic human performance (a goal that can obviously be achieved without reproducing the cognitive mechanisms underlying translation in humans). More on this in the entry on  computational linguistics.