An Inference-Based Model of Word Meaning in Context as a Paraphrase Distribution

被引:3
|
作者
Moon, Taesun [1 ]
Erk, Katrin [1 ]
机构
[1] Univ Texas Austin, Dept Linguist, Austin, TX 78712 USA
关键词
Algorithms; Experimentation; Semantics; lexical semantics; paraphrases; probabilistic graphical models; probabilistic inference; loopy belief propagation; REPRESENTATION;
D O I
10.1145/2483669.2483675
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Graded models of word meaning in context characterize the meaning of individual usages (occurrences) without reference to dictionary senses. We introduce a novel approach that frames the task of computing word meaning in context as a probabilistic inference problem. The model represents the meaning of a word as a probability distribution over potential paraphrases, inferred using an undirected graphical model. Evaluated on paraphrasing tasks, the model achieves state-of-the-art performance.
引用
收藏
页数:28
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