Constructing a hypothesis space from the Web for large-scale Bayesian word learning
Constructing a hypothesis space from the Web for large-scale Bayesian word learning
复制标题
从网络构建用于大规模贝叶斯单词学习的假设空间
DOI：
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发表时间：
2012
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通讯作者：
Thomas L. Griffiths
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作者：
Joshua T. Abbott;Joseph L. Austerweil;Thomas L. Griffiths
美国加州大学伯克利分校心理学系约瑟夫·L·奥斯特韦尔(joseph.austerweil@gmail.com)托马斯·L·格里菲斯(Tom Griffiths@berkeley.edu)美国加州大学伯克利分校心理学系抽象贝叶斯泛化模型在本文中，我们使用这种方法来展示如何从大型在线数据库自动构造假设空间和先验，从而使贝叶斯推广框架应用于广泛的自然刺激成为可能。我们专注于一个特定的泛化问题，单词学习，在这个问题上，人们通过观察几个可以被标记为单词的对象来学习新单词。鉴于一个单词可能扩展的次数本质上是无限的，学习一个单词所指的对象是一个非常困难的归纳问题(Quine，1975)。Xu和Tenenbaum(2007)展示了如何使用贝叶斯泛化框架来解释人们是如何学习新单词的。然而，为了构建他们的贝叶斯模型的假设空间，Xu和Tenenbaum(2007)从他们的参与者那里获得了大约400个相似判断。显然，将这一点扩展到人们学习单词的每一个领域是不现实的。因此，单词学习是探索构建假设空间和先验分布的新方法的合适环境。贝叶斯泛化框架已经成功地解释了人们如何从几个不同的领域将一个属性从几个观察到的刺激概括为新的刺激。为了创建一个成功的贝叶斯泛化模型，建模者通常为每个特定领域指定一个假设空间和先验概率分布。然而，这带来了两个问题：模型的规模没有超出它们设计的(通常是小规模的)领域，模型的解释能力因其对手工编码的假设空间和先验的依赖而降低。为了解决这两个问题，我们提出了一种从大型在线数据库中获取假设空间和先验知识的方法。我们通过从WordNet构建贝叶斯单词学习模型的假设空间和先验来评估我们的方法，WordNet是一个将单词之间的语义关系编码为网络的大型在线数据库。在通过复制先前的单词学习研究来验证我们的方法后，我们将相同的模型应用于一个新的实验，该实验具有三个额外的分类领域(衣服、容器和座椅)。在这两个实验中，我们发现相同的自动构建的假设空间解释了概括行为的复杂模式，在总共六个不同的领域产生了准确的预测。我们提出了一种利用免费在线资源自动构建贝叶斯词汇学习模型的假设空间和先验分布的方法。特别是，我们使用WordNet(Fellbaum，2010；Miller，1995)作为自动创建假设空间的初始来源，使用ImageNet(邓等人，2009)作为自然图像的来源，这些图像可以用作刺激，以在行为实验中测试所得到的模型。Wordnet是一个流行的英语词汇数据库，由100,000多组关系同义词组成。ImageNet是一个符合WordNet层次结构的大型图像本体，其目标是在WordNet中为每个名词提供超过500个高质量的图像。这些资源允许我们为单词学习构建假设空间和先验分布，而不会引发参与者的单一判断，并在比以前可能的规模更大的范围内测试结果模型。我们证明了由WordNet建立的贝叶斯模型捕获了两个行为实验中参与者的判断，解决了前面讨论的贝叶斯模型的实际和理论问题。关键词：概括；概念学习；单词学习；贝叶斯建模；在线数据库简介许多由大脑解决的问题都符合相同的抽象计算公式：如何将一个属性从一组观察到具有该属性的刺激推广到新的刺激？由于有许多方法可以扩展与某些观察到的证据一致的性质，这些都是归纳问题，其中证据约束但不确定问题的解决方案。贝叶斯泛化框架(Shepard，1987；Tenen-Baum&Griffiths，2001)在解释人类在广泛领域的泛化行为方面取得了显著的成功。然而，它的成功在很大程度上取决于假设空间的选择和假设的先验概率分布，这些假设通常是研究人员为每个特定问题手工构建的。这在实践上是不令人满意的，因为这些模型并没有超出最初建模的问题，而且从理论上讲，因为不清楚它们的成功是因为建模者的聪明，而不是因为人们解决的计算问题的深刻的数学性质。一种可能的解决方案是使用关于域的组织的现有信息源作为指定假设空间和先验的基础。这有助于解决国际货币基金组织提出的实际和理论上的关切。在接下来的章节中，我们回顾了贝叶斯泛化模型，然后考察了Xu和Tenenbaum(2007)是如何为他们的贝叶斯词汇学习模型构建假设空间的。然后，我们展示了如何从WordNet构建一个假设空间，该空间可以用于大规模评估单词学习模型。然后，我们提出了两个利用这一假设的实验--
Constructing a hypothesis space from the Web for large-scale Bayesian word learning Joshua T. Abbott (joshua.abbott@berkeley.edu) Joseph L. Austerweil (joseph.austerweil@gmail.com) Thomas L. Griffiths (tom griffiths@berkeley.edu) Department of Psychology, University of California, Berkeley, CA 94720 USA Abstract Bayesian generalization model. In this paper, we use this approach to show how a hypothesis space and prior can be constructed automatically from a large online database, mak- ing it possible to apply the Bayesian generalization frame- work to a wide range of naturalistic stimuli. We focus on one specific generalization problem, word learning, where peo- ple learn new words from observing a few objects that can be labeled with that word. Given that the number of possible ex- tensions of a word is essentially infinite, learning the objects referred to by a word is a very difficult inductive problem (Quine, 1975). Xu and Tenenbaum (2007) showed how the Bayesian generalization framework could be used to explain how people learn new words. However, to construct the hy- pothesis space of their Bayesian model, Xu and Tenenbaum (2007) elicited approximately 400 similarity judgments from their participants. Clearly this is not practical to extend into every domain where people learn words. Thus, word learn- ing is an appropriate setting for exploring novel methods of constructing hypothesis spaces and prior distributions. The Bayesian generalization framework has been successful in explaining how people generalize a property from a few observed stimuli to novel stimuli, across several different domains. To create a successful Bayesian generalization model, modelers typically specify a hypothesis space and prior probability distribution for each specific domain. How- ever, this raises two problems: the models do not scale beyond the (typically small-scale) domain that they were designed for, and the explanatory power of the models is reduced by their reliance on a hand-coded hypothesis space and prior. To solve these two problems, we propose a method for deriving hypothesis spaces and priors from large online databases. We evaluate our method by constructing a hypothesis space and prior for a Bayesian word learning model from WordNet, a large online database that encodes the semantic relationships between words as a network. After validating our approach by replicating a previous word learning study, we apply the same model to a new experiment featuring three additional taxonomic domains (clothing, containers, and seats). In both experiments, we found that the same automatically constructed hypothesis space explains the complex pattern of generalization behavior, producing accurate predictions across a total of six different domains. We propose a method for automatically constructing the hypothesis space and prior distribution of a Bayesian word learning model using freely available online resources. In particular, we use WordNet (Fellbaum, 2010; Miller, 1995) as an initial source for automatically creating the hypothesis space, and ImageNet (Deng et al., 2009) as a source of natu- ralistic images that can be used as stimuli to test the resulting model in behavioral experiments. WordNet is a popular lexi- cal database of English comprised of over 100,000 relational sets of synonyms. ImageNet is a large ontology of images conforming to the hierarchical structure of WordNet, with the aim of providing over 500 high-quality images per noun in WordNet. These resources allow us to construct hypothesis spaces and prior distributions for word learning without elic- iting a single judgment from participants and test the result- ing model on a much larger scale than was previously pos- sible. We demonstrate that the Bayesian model formulated from WordNet captures participant judgments in two behav- ioral experiments, addressing the practical and theoretical is- sues with Bayesian models discussed earlier. Keywords: generalization; concept learning; word learning; Bayesian modeling; online databases Introduction Many problems solved by the mind conform to the same ab- stract computational formulation: How should a property be generalized to novel stimuli from a set of stimuli observed to have the property? As there are many ways to extend the property that are consistent with some observed evidence, these are problems of induction, where the evidence con- strains, but does not determine, the solution to a problem. The Bayesian generalization framework (Shepard, 1987; Tenen- baum & Griffiths, 2001) has been remarkably successful at explaining human generalization behavior in a wide range of domains. However, its success is largely dependent on the choice of a hypothesis space and a prior probability distribu- tion on hypotheses, which are usually hand constructed by the researcher for each specific problem. This is unsatisfy- ing practically, because the models do not scale beyond the originally modeled problem, and theoretically, as it is unclear whether their success is due to the cleverness of the modeler and not because of a deep mathematical property of the com- putational problem that people solve. One possible solution is to use existing sources of infor- mation about the organization of a domain as the basis for specifying a hypothesis space and prior. This helps address both the practical and the theoretical concerns raised by the The plan of the rest of the paper is as follows. In the next sections we review the Bayesian generalization model and then examine how Xu and Tenenbaum (2007) constructed the hypothesis space for their Bayesian word learning model. We then show how to build a hypothesis space from WordNet that can be used to evaluate word learning models on a large scale. Afterwards, we present two experiments utilizing this hypoth-