SGER: Exemplar-based Modeling of Spanish Language Phenomena

SGER：基于范例的西班牙语语言现象建模

• 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

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.

这项研究的目的是两个方面。首先是测试基于典范的语言处理模型的充分性。将利用两个示例模型，语言（AML）和基于蒂尔堡内存的学习者（TIMBL）的类比建模。为此，这些模型将在西班牙语中的几种语言现象进行测试：压力放置，性别分配，减少形成和状态动词变化的选择。将从可用的频率词典和西班牙语语言语料库中提取包含800-2000个实例的数据库。每个实例将被编码为一组语言变量，这些变量与预测手头现象有关。任务是通过使用剩余数据库项作为可能的类似物来预测每个实例的行为。例如，一个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利用根本不同的算法，第二个目的是确定哪些模型可以更好地预测。每个模型将根据上述语言任务的正确预测所讨论的语言行为的能力进行比较。对于某些现象，可以提供涉及语言获取，非CE单词的产生和串行的研究。基于示例模型反映这些研究数据的能力可能会提供可以评估它们的其他测试。