RI: Medium: Collaborative Research: Learning Representations of Language for Domain Adaptation

RI：媒介：协作研究：学习领域适应的语言表示

• 批准号: 1065270
• 负责人: Douglas Downey
• 金额: $ 15万
• 依托单位: Northwestern 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: 2011-04-01 至 2016-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1065270&HistoricalAwards=false
• 关键词: RI; Medium; Collaborative; Research; Learning

Supervised Natural Language Processing (NLP) systems perform poorly on domains and vocabulary that differ from training texts. A growing body of empirical and theoretical work points to the features used by traditional NLP systems as the culprit for domain-dependence and for the inability to generalize to previously unseen words. This project is the first to systematically investigate representation-learning as a technique for improving performance on domain adaptation. It explores latent-variable language models ? including Factorial Hidden Markov Models, dependency parsing models, and deep architectures ? as techniques for extracting novel features from text. The resulting representations yield similar features for distributionally-similar words, thereby allowing generalization to words not seen during training of a classifier. The project also explores novel procedures for training a language model, which incorporate Web-scale ngram statistics as substitutes for standard statistics used in unsupervised training.Language users are extraordinarily inventive, and new domains of discourse appear constantly, such as in specialized areas of science and technology. By building on top of the representations produced by this project, NLP systems can improve in accuracy on new domains and on Web text, bringing applications like the Semantic Web closer to reality. For resource-poor languages and domains, the project can help reduce the cost of annotating texts by reducing the need for broad coverage in the training texts. By involving the diverse student bodies at Temple University and Philadelphia-area high schools, the project helps to broaden participation in computer science research by underrepresented groups.

有监督的自然语言处理 (NLP) 系统在与训练文本不同的领域和词汇上表现不佳。 越来越多的实证和理论工作指出，传统 NLP 系统使用的特征是领域依赖性和无法泛化到以前未见过的单词的罪魁祸首。该项目是第一个系统地研究表征学习作为提高领域适应性能的技术的项目。 它探索潜变量语言模型？包括阶乘隐马尔可夫模型、依赖解析模型和深层架构？作为从文本中提取新颖特征的技术。 由此产生的表示对于分布相似的单词产生相似的特征，从而允许泛化到分类器训练期间未见过的单词。 该项目还探索了训练语言模型的新程序，其中结合了网络规模的 ngram 统计数据作为无监督训练中使用的标准统计数据的替代品。语言使用者具有非凡的创造力，新的讨论领域不断出现，例如在专门的科学领域和技术。 通过建立在该项目生成的表示之上，NLP 系统可以提高新领域和 Web 文本的准确性，使语义 Web 等应用程序更接近现实。 对于资源匮乏的语言和领域，该项目可以通过减少培训文本中广泛覆盖的需求来帮助降低注释文本的成本。 通过让坦普尔大学和费城地区高中的多元化学生团体参与进来，该项目有助于扩大代表性不足的群体对计算机科学研究的参与。