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SGER: Exemplar-based Modeling of Spanish Language Phenomena

SGER：基于范例的西班牙语语言现象建模

基本信息

批准号：
0082195
负责人：
David Eddington
金额：
$ 2.75万
依托单位：
Mississippi State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2000
资助国家：
美国
起止时间：
2000-08-15 至 2001-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=0082195&HistoricalAwards=false
关键词：
SGER Exemplar based Modeling Spanish

项目摘要

The goal of this research is two-fold. The first is to test the adequacy of exemplar-based models of language processing. Two exemplar models will be utilized, Analogical Modeling of Language (AML) and Tilburg Memory-based Learner (TiMBL). To this end, these models will be tested on several linguistic phenomena in Spanish: stress placement, gender assignment, diminutive formation, and selection of change of state verb. A database containing 800-2000 instances of each phenomenon will be extracted from available frequency dictionaries and Spanish language corpora. Each instance will be encoded as a set of linguistic variables that are pertinent to predicting the phenomenon at hand. The task will be to predict the behavior of each instance by using the remaining database items as possible analogs. For example, the gender of a given noun will be predicted on the basis of its similarities to other nouns in the database.Since AML and TiMBL utilize radically different algorithms, the second aim is to determine which model makes better predictions. Each model will be compared on the above-mentioned language tasks in terms of its ability to correctly predict the linguistic behavior in question. For some of the phenomena, studies involving language acquisition, production of nonce words, and slips-of-the-tongue, are available. The ability of the exemplar-based models to mirror the data from these studies may provide additional tests on which they may be assessed.

这项研究的目的有两个。第一个是测试基于范例的语言处理模型的充分性。将使用两个样本模型，语言的类比建模(AML)和基于Tilburg Memory的学习者(Tilburg Memory-Based Learner)。为此，这些模型将在西班牙语中的几种语言现象上进行测试：重音位置、性别分配、小词组和状态动词的选择。将从现有的频率词典和西班牙语语料库中提取一个数据库，其中包含每个现象的800-2000个实例。每个实例将被编码为一组语言变量，这些变量与预测手头的现象有关。任务将是通过使用剩余的数据库项作为可能的类比来预测每个实例的行为。例如，给定名词的性别将根据其与数据库中其他名词的相似性进行预测。由于AML和TimBL使用完全不同的算法，第二个目标是确定哪个模型做出了更好的预测。每个模型都将在上述语言任务上进行比较，以判断其正确预测所讨论的语言行为的能力。对于其中的一些现象，有关于语言习得、随机词的产生和口误的研究。基于样本的模型反映这些研究的数据的能力可能会提供额外的测试，这些测试可能会对它们进行评估。