What do you know about an alligator when you know the company it keeps?
What do you know about an alligator when you know the company it keeps?
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DOI：
10.3765/sp.9.17
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发表时间：
2016-01-01
影响因子：
1.1
通讯作者：
Erk, Katrin
中科院分区：
文献类型：
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作者：
Erk, Katrin
分布模型根据观察到的上下文描述单词的含义。他们在计算语言学方面非常成功。它们也被认为是人类如何获得（部分）词义知识的模型。但这就提出了一个问题，即分布模型究竟可以学习什么，以及分布信息如何与智能体所知道的其他一切进行交互。对于第一个问题，我建立在最近的工作基础上，该工作表明分布模型实际上可以在一定程度上区分语义关系，并认为（正确的）分布相似性表明属性重叠。对于第二个问题，我建议，如果一个代理人不知道什么是鳄鱼，但知道鳄鱼是类似于鳄鱼，代理人可以概率推断鳄鱼的属性从鳄鱼的已知属性。分布证据是嘈杂和局部的，所以我采用了语义知识的概率帐户，可以从这样的数据中学习。
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.