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: 2013
• 资助国家: 加拿大
• 起止时间: 2013-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=524105
• 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.

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。