Like other types of underspecification, metonymy has been approached both from knowledge-based and corpus-based perspectives. Knowledge that can be brought to bear includes selectional preferences (e.g., companies in general do not literally rise),  lexical concept hierarchies (e.g., as provided by WordNet), generic knowledge about the types of metonymy relations commonly encountered, such as part-for-whole, place-for-event, object-for-user, producer-for-product, etc. (Lakoff and Johnson 1980), rules for when to conjecture such relations (e.g., Weischedel and Sondheimer 1983), named-entity knowledge, and knowledge about related entities (e.g., a company may have a stock market index, and this index may rise or fall) (e.g., Bouaud et al. 1996; Onyshkevych 1998).  Corpus-based methods (e.g., see Markert and Nissim 2007) often employ many of these knowledge resources, along with linguistic and statistical features such as POS tags, dependency paths and collocations in the vicinity of a potential metonym. As for other facets of the interpretive process (including parsing), use of deep domain knowledge for metonym processing can be quite effective in sufficiently narrow domains, while corpus-based, shallow methods scale better to broader domains, but are apt to reach a performance plateau falling well short of human standards.