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

来源：

ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY | 2013年 / 4卷 / 03期

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

页数：28

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