The most actively researched approach to knowledge extraction from text in the last two decades has been the indirect one, beginning with a paper by Marti Hearst demonstrating that hyponymy relations could be rather simply and effectively discovered by the use of lexicosyntactic extraction patterns (Hearst 1992). For example, extraction patterns that look for noun phrases separated by “such as” or “and other” will match word sequences like “seabirds such as penguins and albatrosses” or “beans, nuts, and other legumes”, leading to hypotheses that seabird is a hypernym of penguin and albatross, and that beans and nuts are hyponyms of legumes.  By looking for known hyponym-hypernym pairs in close proximity, Hearst was able to expand the initial set of extraction patterns and hence the set of hypotheses. Many variants have been developed since then, with improvements such as automation of the bootstrapping and pattern discovery methods, often with machine learning techniques applied to selection, weighting and combination of local features in the immediate vicinity of the relata of interest. Relations other than hyponymy that have been targeted, of relevance to language understanding, include part-of relations, causal relations, and telic relations  (such as that the use of milk is to drink it).  