Probabilistic Approaches to Learning the Semantics and Syntax of Words

学习单词语义和句法的概率方法

• 批准号: 227787-2012
• 负责人: Stevenson, Suzanne
• 金额: $ 2.48万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=570320
• 关键词: Probabilistic; Approaches; Learning; Semantics; Syntax

We have billions of words of online text available at our fingertips, yet tools for effectively processing it - going beyond merely finding documents, to truly understanding them - are sorely lacking. A primary obstacle is the inherent flexibility of words. People are continually extending the meaning of words and combining them productively in previously unseen ways. Current natural language processing (NLP) systems, however, rely largely on static lexical resources - electronic dictionaries and ontologies - that are unable to support the needed flexibility and adaptability for understanding text. This proposal focuses on computational methods for learning rich semantic and syntactic knowledge about words, using robust probabilistic representations that capture the flexibility of words and are adaptable to new usages. We approach this problem from two complementary perspectives. First, we ask how it is that very young children learn the complex information about words so effortlessly, and yet such understanding has proven elusive to NLP systems. We develop computational models of child word learning that help us to better understand this uniquely-human ability. Our models play an important role in the scientific study of cognition, by contributing precise, falsifiable theories of human language-learning mechanisms, and also contribute potential algorithms for use in NLP systems. Second, we develop novel techniques for automatically building and extending large-scale lexical resources for NLP. We adapt advanced statistical machine learning techniques to our tasks by incorporating linguistic and cognitive knowledge, enabling us to automatically infer richer information about words that can support more flexible and adaptable NLP tools than is currently possible. We also integrate linguistic and visual information in the processing of multimodal documents to contribute improved algorithms for automatic annotation of images with keywords to support more efficient image search.

我们的指尖有数十亿字的在线文本，但有效处理这些文本的工具--不仅仅是查找文档，而是真正理解它们--却非常缺乏。一个主要的障碍是词语固有的灵活性。人们不断地扩展词语的含义，并以前所未有的方式将它们有效地结合起来。然而，当前的自然语言处理（NLP）系统在很大程度上依赖于静态词汇资源-电子词典和本体-无法支持理解文本所需的灵活性和适应性。该建议侧重于学习丰富的语义和句法知识的单词的计算方法，使用强大的概率表示，捕捉单词的灵活性，并适应新的用法。 我们从两个互补的角度来处理这个问题。首先，我们要问的是，很小的孩子是如何如此轻松地学习关于单词的复杂信息的，而这种理解对NLP系统来说是难以实现的。我们开发了儿童词汇学习的计算模型，帮助我们更好地理解这种人类特有的能力。我们的模型在认知科学研究中发挥着重要作用，为人类语言学习机制提供了精确的、可证伪的理论，也为NLP系统提供了潜在的算法。其次，我们开发了新的技术，自动构建和扩展大规模的词汇资源的自然语言处理。我们通过整合语言和认知知识，使先进的统计机器学习技术适应我们的任务，使我们能够自动推断出有关单词的更丰富的信息，这些信息可以支持比目前更灵活和适应性更强的NLP工具。我们还整合了语言和视觉信息的多模态文档的处理，以改进算法，自动注释图像的关键字，以支持更有效的图像搜索。