RI: Parsing Models and Algorithms for Morphologically Rich Languages

自然语言处理（NLP）领域迄今为止主要致力于英语的技术，尽管英语是一个类型学上的离群值，世界上大多数人都不会说英语。本项目旨在开发统计自然语言分析工具，以消除非英语文本的形态和句法结构的歧义。 具体而言，试点研究的目标是设计，培训，实施和传播统计形态句法分析模型，阿拉伯语和希伯来语。该项目从一个简单的形式主义（统计头部自动机语法）开始，并利用新颖的判别学习方法来构建可以轻松移植到新数据集的模型。虽然以前的工作已经简化了问题，假设完美的形态消歧之前，解析，对于大多数语言，准确的形态消歧尚未提供;本项目的目的是将形态消歧到解析算法，以提高这两项任务的准确性。影响：该项目将通过直接推进5亿多人使用的语言的核心语言处理技术，并由于语言可移植性原则，通过促进更多语言的未来工作，改善全球获取信息的机会。 预计该项目将提高所考虑语言的解析准确性的最新水平，开发的模型和算法将免费提供用于研究目的。 这些工具有望帮助研究机器翻译和多语言信息提取等应用技术的研究人员。

The field of natural language processing (NLP) has, to date, largely focused its efforts on technology for English, even though it is a typological outlier and the majority of the world's people do not speak it. This project aims to develop statistical natural language analysis tools to disambiguate the morphological and syntactic structure of non-English text. Specifically, the objective of the pilot study is to design, train, implement, and disseminate statistical morpho-syntactic parsing models for Arabic and Hebrew. This project starts with a straightforward formalism (statistical head automaton grammars) and makes use of novel discriminative learning methods to build models that can be easily ported to new datasets. While previous work has simplified the problem by assuming perfect morphological disambiguation prior to parsing, for most languages, accurate morphological disambiguation is not yet available; this project aims to integrate morphological disambiguation into the parsing algorithm for better accuracy on both tasks. Impact: This project will improve global access to information by directly advancing core language processing technology in languages spoken by more than half a billion people and - because of the language-portability principle - by facilitating future work on many more languages. It is expected that this project will improve the state-of-the-art in parsing accuracy for the languages under consideration, and the models and algorithms developed will be made freely available for research purposes. These tools are expected to aid researchers working on applied technologies such as machine translation and multilingual information extraction.