CAREER: Flexible Learning for Natural Language Processing

统计学习现在是自然语言处理（NLP）的核心。 弥合学习和语言表征之间的差距需要超越学习参数。 这个CAREER项目解决了三个具有挑战性的，未解决的问题：1。考虑到最近在学习语言模型参数和近似推理方面的进展，特征设计的过程如何实现自动化？2.鉴于NLP任务的定义通常不依赖于实际应用，并且特定的注释数据集不太可能满足多个NLP项目的需求，学习框架是否可以扩展到执行自动任务细化，简化语言分析任务，以获得更一致，更精确或更快的性能？3.语言的计算模型能否考虑到我们的语言数据所嵌入的非文本语境？ 基于最近在社会文本分析和文本驱动预测方面的成功，这个CAREER项目寻求利用上下文来完善语言结构模型，同时实现这一应用领域的进步。这个基础研究支持了广泛的语言工程应用和离散数据分析的进步。 除了核心的研究进展，这个CAREER项目还贡献了一个新的公开可用的语法分析器，它模拟了语法结构中最一致的可学习元素。 教育活动包括一个新的项目-基于PI的本科NLP课程中的文本驱动预测和一个新的机器学习本科课程。它支持PI参与外展活动，以高中学生和CMU更广泛的学生，通过在非CS课堂上展示他的研究方面。

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.