The structure-mapping engine: algorithm and examples
Artificial Intelligence
A computational model of metaphor interpretation
A computational model of metaphor interpretation
WordNet: a lexical database for English
Communications of the ACM
Knowledge Representation and Metaphor
Knowledge Representation and Metaphor
Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
met*: a method for discriminating metonymy and metaphor by computer
Computational Linguistics
Evaluating WordNet-based Measures of Lexical Semantic Relatedness
Computational Linguistics
Hownet And the Computation of Meaning
Hownet And the Computation of Meaning
Analogy generation with HowNet
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
| Hi-index | 0.00 |
An effective speaker can use metaphor to communicate a wealth of propositions and affective attitudes with a single juxtaposition of ideas [12,8,6,10,7,3,15]. But as such, an effective metaphor requires effective communication, which in turn requires that the speaker has a clear idea of the content to be communicated, and an equally clear understanding of which conceptual vehicles best communicate this content. We present here a concise corpusderived meaning representation for metaphor processing that captures the most widely-used talking points that are evoked in everyday metaphors and similes. We illustrate how these talking points can be acquired by harvesting the web, and further show how comparable but discretely different talking points can be reconciled during metaphor processing. Finally, by replicating the clustering experiments of [1], we show that talking points yield an especially concise representation of concepts in general.