Probabilistic Approaches to Learning the Semantics and Syntax of Words

我们有数十亿字的在线文本可以在我们的指尖上使用，但有效处理它的工具--不仅仅是查找文档，到真正理解它们--严重缺乏。一个主要的障碍是词语固有的灵活性。人们不断地扩展单词的含义，并以前所未有的方式富有成效地将它们组合在一起。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。该建议侧重于学习有关单词的丰富语义和句法知识的计算方法，使用健壮的概率表示法来捕捉单词的灵活性并适应新的用法。

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.