Please Scroll down for Article Language and Cognitive Processes a Computational Model of Learning Semantic Roles from Child-directed Language a Computational Model of Learning Semantic Roles from Child-directed Language
Please Scroll down for Article Language and Cognitive Processes a Computational Model of Learning Semantic Roles from Child-directed Language a Computational Model of Learning Semantic Roles from Child-directed Language
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请向下滚动查看文章语言和认知过程从儿童导向语言学习语义角色的计算模型从儿童导向语言学习语义角色的计算模型
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通讯作者：
S. Stevenson
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作者：
A. Alishahi;S. Stevenson
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This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, redistribution , reselling , loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. Semantic roles are a critical aspect of linguistic knowledge because they indicate the relations of the participants in an event to the main predicate. Experimental studies on children and adults show that both groups use associations between general semantic roles such as Agent and Theme, and grammatical positions such as Subject and Object, even in the absence of familiar verbs. Other studies suggest that semantic roles evolve over time, and might best be viewed as a collection of verb-based or general semantic properties. A usage-based account of language acquisition suggests that general roles and their association with grammatical positions can be learned from the data children are exposed to, through a process of generalisation and categorisation. In this paper, we propose a probabilistic usage-based model of semantic role learning. Our model can acquire associations between the semantic properties of the arguments of an event, and the syntactic positions that the arguments appear in. These probabilistic associations enable the model to learn general conceptions of roles, based only on exposure to individual verb usages, and without requiring explicit labelling of the roles in the input. The acquired role properties are a good intuitive match to the expected properties of various roles, and are useful in guiding comprehension in the model to the most likely Toronto for the helpful discussions. This article is an extended version of a paper that appeared in the proceedings of EuroCogSci 2007. We wish to thank the anonymous reviewers of that paper as well as those of this article for their insightful comments and recommendations. We are also grateful for the financial support from the interpretation in the face of ambiguity. The learned roles can also be used to select the correct …