权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

CRII: RI: Learning a Timely Semantic Resource from Social Media Data

CRII：RI：从社交媒体数据中学习及时的语义资源

基本信息

批准号：
1755898
负责人：
Wei Xu
金额：
$ 17.5万
依托单位：
Ohio State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-06-01 至 2020-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1755898&HistoricalAwards=false
关键词：
CRII RI Learning Timely Semantic

项目摘要

One key challenge in text mining and natural language processing research is that a single meaning can be expressed in many different ways, i.e., paraphrases. There has been steady progress towards large paraphrase resources, and a significant increase in its applications: from information retrieval, information extraction, and natural language generation to IBM's Watson, Google's Knowledge Graph, and many more. This research aims to create better paraphrase acquisition techniques and larger scale semantic resources, which could be of great use in various natural language processing tasks and social media data analytics in social science, national security, and other related fields. One example of potential applications is text simplification, which automatically rephrases complex texts into simpler language for children or people with reading disabilities.The technical innovation of this study focuses on joint modeling of word- and phrase-level alignments between sentence pairs to address the challenges of extracting semantic knowledge from informal data sources (such as social media), which exist in very large quantities rather than just formal sources, such as newswire as per previous work. The model design extends multiple instance learning via two methods, a graphical model and neural network, and can flexibly permit the exploration of different assumptions and models the importance of words or phrases. The modeling advancements can be generalized to other natural language understanding tasks, which require analyzing sentences based on word-level composition or word meaning in a given context, and natural language generation tasks that benefit from learning what words and phrases to remove or rephrase.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

文本挖掘和自然语言处理研究中的一个关键挑战是，单个含义可以以许多不同的方式表达，即，释义大型释义资源已经取得了稳步的进展，其应用也有了显着的增长：从信息检索，信息提取和自然语言生成到IBM的沃森，谷歌的知识图等等。这项研究旨在创建更好的释义获取技术和更大规模的语义资源，这可能在社会科学，国家安全和其他相关领域的各种自然语言处理任务和社交媒体数据分析中有很大的用处。文本简化是一个潜在的应用，它可以自动地将复杂的文本改写成更简单的语言，以供儿童或阅读障碍者使用。本研究的技术创新在于对句子对之间的单词和短语级对齐进行联合建模，以解决从非正式数据源中提取语义知识的挑战（如社交媒体），存在于非常大的数量，而不仅仅是正式的来源，如根据以前的工作新闻专线。模型设计通过两种方法扩展了多实例学习，即图形模型和神经网络，并且可以灵活地允许探索不同的假设和模型单词或短语的重要性。建模的进步可以推广到其他自然语言理解任务，这些任务需要在给定的上下文中基于词级组成或词义来分析句子，该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响进行评估来支持审查标准。