Charting lexical development through dense coding and analysis of word senses

孩子们学习单词的速度非常快，这一点更令人惊讶，因为大多数单词都非常含糊。具体地说，大多数单词都是“多义词”，这意味着它们可以用在一系列不同但相关的意义上。例如，想一想单词“run”的所有不同用法：你可以跑一场比赛，跑一辆汽车，经营一家公司，运行一些测试，甚至是洗澡。这些灵活的单词用法为我们提供了一种生动的表现力--我们可以使用一个单词的一个含义，而暗示其他所有含义--但大多数研究人员倾向于认为，一词多义也会使单词难以学习。特别是，当一个单词的意义在不同的用途中发生变化时，那么孩子们应该如何掌握它呢？我们认为这个假设是倒退的，灵活使用单词实际上有助于儿童习得词汇。这是因为单词的意义是相互关联的(例如，在“Run a Car”和“Run a Company”中，Run的意义之间存在明显的联系)，所以一旦孩子学会了一个单词的一个意义，他们就可以利用这些知识作为一个支持性的平台，更容易地推断出新的意义。相比之下，如果所有的单词都是明确的，或者所有的单词都有不相关的歧义，那么每个新的含义都必须在没有任何支持的情况下学习。因此，尽管一词多义从外表上看可能令人困惑，但从孩子的角度来看，它实际上是一个学习的机会。这个项目测试了这一想法的三个含义。首先，一词多义有助于学习，然后我们期望它在儿童接受的输入中应该是充足的。然而，我们不知道孩子们听到了多少一词多义。事实上，如果照顾者倾向于说简单而明确的话，他们可能会含蓄地避免一词多义，特别是对年幼的孩子。其次，如果一词多义确实有助于学习，那么我们应该期待孩子们也会使用一词多义，即使在他们说的第一个单词中也是如此。最后，如果一词多义有助于学习，那么我们应该期待孩子们更容易学习新的词义，而不是学习全新的单词。为了测试这些含义，我们需要一个数据库，记录照顾者和孩子如何使用多义词，指定在什么时候使用哪些意义。然而，尽管有关于照顾者和儿童如何互动的现有数据库，但它们从未具体说明词语使用的不同含义。例如，当使用“run”时，语料库从未明确说明其含义。因此，本研究的第一个目的是从词义的角度重新编码这些儿童语言数据库。为此，我们将使用我们团队开发的新工具包，并将其应用于英语和法语数据。据我们所知，这将是迄今构建的最大的带有意义注释的数据库，不仅是在儿童语言方面，而且在更广泛的人类对话方面。使用这个数据库，我们将研究照顾者在与他们的孩子交谈时如何使用一词多义，以及孩子们在他们最早的语言使用中如何使用一词多义。我们将建立新的统计模型，评估父母在与最小的孩子交谈时是否避免使用一词多义，以及孩子是否发现更容易使用新词义来学习旧单词，或者完全学习新单词。这些数据和分析应该会提供迄今为止关于儿童如何获得一词多义的最清晰的图景，对语言发展理论、语言学理论以及我们对照顾者如何与孩子互动的理解都有影响。我们将公开我们的工具和数据库，并在学术期刊上描述我们的结果。

Children learn words at a remarkable rate, which is all the more striking because most words are highly ambiguous. Specifically, most words are "polysemous", which means that they can be used in a range of distinct but related senses. For instance, think of all the different ways that the word "run" can be used: You can run a race, run a car, run a company, run some tests, or even run a bath. These flexible uses of words provide us with a vivid expressive power -- we can use a word with one meaning while alluding to all the rest -- but most researchers have tended to assume that polysemy also makes words hard to learn. In particular, when a word's meaning changes across uses, then how are children ever supposed to acquire it?We have argued that this assumption is backwards, and that flexible uses of words actually help children to acquire a vocabulary. That is because the senses of words are related to one another (e.g., there is an obvious link between the senses of run in "run a car" versus "run a company"), and so once a child has learned one sense of a word, then they can use that knowledge as a supportive platform to more easily infer new senses. By contrast, if all words were unambiguous, or all words had unrelated ambiguous meanings, then each new meaning would have to be learned without any support. Thus, while polysemy may seem confusing from the outside, it is actually an opportunity for learning, when seen from the perspective of a child. This project tests three implications of this idea. First, for polysemy to help learning, then we expect that it should be plentiful in the input that children receive. However, we do not know how much polysemy children hear. Indeed, it is possible that caregivers implicitly avoid polysemy, particularly with younger children, if they tend to say simple and unambiguous words. Second, if polysemy indeed helps learning, then we should expect that children also use polysemy, even in the first words that they say. Finally, if polysemy helps learning, then we should expect that it will be easier for children to learn new senses for words, than to learn entirely new words.To test these implications, we need a database of how caregivers and children use polysemous words, that specifies which senses are used when. However, while there are existing databases of how caregivers and children interact, they never specify the different senses in which words are used. For instance, when "run" is used, the corpora never explicitly specify with which meaning. Hence, quantitative analyses of polysemy are currently impossible.Thus, the first aim of this research is to recode these databases of child language in terms of word senses. To do this, we will use a new toolkit that our group has developed, and we will apply it to both English and French data. The output will be (to our knowledge) the largest sense-annotated database yet constructed, not only in terms of child language, but in terms of human conversations more broadly. Using this database, we will then examine how caregivers use polysemy when talking to their children, and how children use polysemy in their earliest language use. We will build novel statistical models that assess questions such as whether parents avoid using polysemy when talking to their youngest children, and whether children find it easier to either use new senses for old words, or learn entirely new words altogether.These data and analyses should provide the clearest picture to date of how children acquire polysemy, with implications for theories of language development, theories from linguistics, and for our understanding of how caregivers interact with their children. We will make our tools and databases publicly available, and describe our results in scholarly journals.