CAREER: Flexible Learning for Natural Language Processing

职业：自然语言处理的灵活学习

• 批准号: 1054319
• 负责人: Noah Smith
• 金额: $ 54.98万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-02-01 至 2016-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1054319&HistoricalAwards=false
• 关键词: CAREER; Flexible; Learning; Natural; Language

Statistical learning is now central to natural language processing(NLP). Bridging the gap between learning and linguisticrepresentation requires going beyond learning parameters. This CAREERproject addresses three challenging, unresolved questions:1. Given recent advances in learning the parameters of linguisticmodels and in approximate inference, how can the process of featuredesign be automated?2. Given that NLP tasks are often defined without recourse to realapplications and that a specific annotated dataset is unlikely tofulfill the needs of multiple NLP projects, can learning frameworks beextended to perform automatic task refinement, simplifying alinguistic analysis task to obtain more consistent, more precise, orfaster performance?3. Can computational models of language take into account the non-textcontext in which our linguistic data are embedded? Building on recentsuccess in social text analysis and text-driven forecasting, thisCAREER project seeks to exploit context to refine models of linguisticstructure while enabling advances in this application area.This basic research supports advances in a wide range of languageengineering applications and discrete data analysis. In addition tocore research advances, this CAREER project contributes a newpublicly-available parser that models the most consistently learnableelements of syntactic struture. Educational activities include a newproject-based on text-driven forecasting within the PI's undergraduateNLP course and a new undergraduate course in machine learning. Itsupports involvement by the PI in outreach activities to high schoolstudents and to a wider range of students at CMU by exposing aspectsof his research in non-CS classrooms.

统计学习现在是自然语言处理（NLP）的核心。 弥合学习和语言表现之间的差距，需要超越学习参数。 该职业项目解决了三个具有挑战性的未解决的问题：1。考虑到学习语言模型的参数和大致推断的最新进展，如何自动化特色的过程？2。鉴于NLP任务通常是在不求助于重新应用的情况下定义的，并且特定的注释数据集不太可能是多个NLP项目的需求，因此学习框架是否可以进行自动任务改进，简化了静态分析任务以获得更加一致，更精确，更精确，ORFASTER性能？3。语言的计算模型是否可以考虑到我们的语言数据嵌入的非文学文本？ 在社交文本分析和文本驱动的预测中，基于RecentSuccess，该项目旨在利用上下文来完善语言学结构模型，同时在该应用领域实现进步。这项基础研究支持各种语言启动应用程序的进步和离散数据分析。 此外，此外，该职业项目还贡献了一个新公共可用的解析器，该解析器模拟了句法曲线最始终如一的学习元素。 教育活动包括一项基于新的Protion预测的基于新的项目，其中PI的本科课程以及机器学习的新本科课程。 PI通过暴露其在非CS教室中的研究方面的各个方面，向高中生和更广泛的CMU学生参与了其参与。