文章基本信息

标题：What do you know about an alligator when you know the company it keeps?
本地全文：下载
作者：Katrin Erk
期刊名称：Semantics and Pragmatics
电子版ISSN：1937-8912
出版年度：2016
卷号：9
页码：17:1-63
DOI：10.3765/sp.9.17
语种：English
出版社：Linguistic Society of America
摘要：Distributional models describe the meaning of a word in terms of its observed contexts. They have been very successful in computational linguistics. They have also been suggested as a model for how humans acquire (partial) knowledge about word meanings. But that raises the question of what, exactly, distributional models can learn, and the question of how distributional information would interact with everything else that an agent knows. For the first question, I build on recent work that indicates that distributional models can in fact distinguish to some extent between semantic relations, and argue that (the right kind of) distributional similarity indicates property overlap. For the second question, I suggest that if an agent does not know what an alligator is but knows that alligator is similar to crocodile , the agent can probabilistically infer properties of alligators from known properties of crocodiles. Distributional evidence is noisy and partial, so I adopt a probabilistic account of semantic knowledge that can learn from such data. BibTeX info
其他摘要：Distributional models describe the meaning of a word in terms of its observed contexts. They have been very successful in computational linguistics. They have also been suggested as a model for how humans acquire (partial) knowledge about word meanings. But that raises the question of what, exactly, distributional models can learn, and the question of how distributional information would interact with everything else that an agent knows. For the first question, I build on recent work that indicates that distributional models can in fact distinguish to some extent between semantic relations, and argue that (the right kind of) distributional similarity indicates property overlap. For the second question, I suggest that if an agent does not know what an alligator is but knows that alligator is similar to crocodile , the agent can probabilistically infer properties of alligators from known properties of crocodiles. Distributional evidence is noisy and partial, so I adopt a probabilistic account of semantic knowledge that can learn from such data. BibTeX info
关键词：distributional semantics;distributional semantics and formal semantics;language learning