This paper focuses on just the task of metaphor identification, although they consider metaphors expressed by nouns, adjectives, and verbs, unlike much of the related work which looks at only nouns or only verbs. The authors first discuss various general challenges in the task. They cover context-sensitive metaphors, metaphors that require reference resolution to identify, and metaphors that are not identifiable using lexical semantics alone. The authors then restrict their attention to three forms of metaphor: noun1-IsA-noun2, verb-object, and adjective-noun. To determine if a given sentence is metaphorical, they parse the sentence using Klein and Manning's unlexicalized phrase-structure parser, then look for each form of metaphor separately. To find the first type, they use a the following simple heuristic: if two nouns are in an IsA relationship and the latter is not a hypernym of the former, the sentence is metaphorical. The second two types are both done using the same method. For a predicate-noun pair, a corpus is searched for every instance of that predicate, and the probability of each noun being its argument is computed. If neither the target noun nor any of its hyponyms has a high enough probability in this distribution, the sentence is marked as metaphorical.
The methods this paper uses are fairly crude, and they do not do a very good job of explaining their evaluation. They appear to use data that they annotated with WordNet in mind, which seems problematic, since they use WordNet as a resource when running on the test set. They do not report their accuracy clearly, but they appear to get F measures in the 55-65% range.