Literal (computer programming)

Abstract: These experiments concern the comprehension of idiomatic expressions. The hypothesis was that there are distinct idiomatic and literal modes of processing sentences. In two experiments, 414 undergraduates read a series of sentences containing either literal or idiomatic ambiguities and then a test which had both a literal and an idiomatic meaning. Ss recorded, which meaning they perceived first. Taken together, the experiments indicate that inducing a set to perceive idioms can increase the proportion of people seeing the idiomatic meaning of test sentence first and a set to perceive literal meanings can reduce this proportion compared to a no-set baseline. Since the procedures to induce set did not involve grammatical or semantic information relevant to comprehension of the test sentence, these results suggest the existence of distinct literal and idiomatic processing strategies.

Abstract: Metaphor is ubiquitous in text, even in highly technical text. Correct inference about textual entailment requires computers to distinguish the literal and metaphorical senses of a word. Past work has treated this problem as a classical word sense disambiguation task. In this paper, we take a new approach, based on research in cognitive linguistics that views metaphor as a method for transferring knowledge from a familiar, well-understood, or concrete domain to an unfamiliar, less understood, or more abstract domain. This view leads to the hypothesis that metaphorical word usage is correlated with the degree of abstractness of the word's context. We introduce an algorithm that uses this hypothesis to classify a word sense in a given context as either literal (denotative) or metaphorical (connotative). We evaluate this algorithm with a set of adjective-noun phrases (e.g., in dark comedy, the adjective dark is used metaphorically; in dark hair, it is used literally) and with the TroFi (Trope Finder) Example Base of literal and nonliteral usage for fifty verbs. We achieve state-of-the-art performance on both datasets.

Abstract: We describe a technique for determining the thresholds for the appearance of cores in random structures. We use it to determine (i) the threshold for the appearance of a k-core in a random r-uniform hypergraph for all r, k ≥ 2, r + k > 4, and (ii) the threshold for the pure literal rule to find a satisfying assignment for a random instance of r-SAT, r ≥ 3. © 2005 Wiley Periodicals, Inc. Random Struct. Alg., 2005