RI-Small: Exploiting Syntactic, Semantic and Lexical Regularities in Statistical Language Modeling

该项目的目标是建立一个统计语言模型，能够捕获自然语言的各种错误，主要是局部词汇和长范围句法，或语义错误，以提高各种自然语言应用程序的性能。它是在有向马尔可夫随机场范式下进行的，以顺序嵌入更高级的句法结构和/或语义主题成分，并形成自然语言的复杂分布。通过利用每个复合语言模型的特殊结构，将看似复杂的统计表示分解为简单的表示;这使得用于较简单的复合语言模型的估计和推理算法能够成为用于复杂复合语言模型的估计的内部构建块，从而最终解决了极其复杂的，复合语言模型是可扩展的，并且可能显著提高最先进的语音识别和机器翻译系统的性能，这将构成对语言建模研究的重要贡献。该项目中开发的技术不仅可能导致有效，健壮和智能的语言技术应用，而且还可能扩展并应用于解决计算生物学和计算机视觉中的问题。该项目为信息技术跨学科教育提供了良好的环境，连接语言和语音处理，机器学习和计算统计以及理论计算机科学领域，使各级学生受益。

This project's aim is to build a statistical language model that is able to capture various kinds of regularities of natural language, mainly local lexical and long range syntactic, or semantic regularities to improve the performance of various natural language applications. It is conducted under the directed Markov random field paradigm to sequentially embed more advanced syntactic structure and/or semantic topic components plus to form complex distributions for natural language. By exploiting the particular structure of each composite language model, the seemingly complex statistical representations are decomposed into simpler ones; this enables the estimation and inference algorithms for the simpler composite language models to become internal building blocks for the estimation of complex composite language models, thus finally solving the estimation problem for extremely complex, high-dimensional distributions.The composite language models are scalable and might significantly increase the performance of the state-of-the-art speech recognition and machine translation systems which would constitute an important contribution to the language modeling research. The techniques developed in this project might not only lead to effective, robust and intelligent language technology applications, but also might be extended and applied to solve problems in computational biology and computer vision. The project provides an excellent environment for interdisciplinary education in information technology that bridges areas of language and speech processing, machine learning and computational statistics, and theoretical computer science to benefit students of all levels.