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RI: Small: Robust Models for Sequence Labelling in Social Media Data

RI：小型：社交媒体数据中序列标记的稳健模型

基本信息

批准号：
1910192
负责人：
Thamar Solorio
金额：
$ 30.79万
依托单位：
University of Houston
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1910192&HistoricalAwards=false
关键词：
RI Small Robust Models Sequence

项目摘要

In the last decade social media platforms have increased their impact on the way people communicate; these platforms are now considered an essential communication tool that people use broadly to share information, but also to get informed about the latest events on any topic. Consequently, the information running through those platforms, generated by users, companies, the media, and political entities, is extremely relevant to understand current events, behaviors, and more, and the automated distillation of this data is of great practical value. Current technology for text processing fails to perform information extraction accurately on social media data since these sophisticated algorithms have been trained on highly edited English text with a narrow set of topics, such as that in newswire data. In contrast, social media data has a fluid grammar, a very large vocabulary, unlimited topics, and includes multiple languages that are often mixed in the same text. This project addresses the many challenges involved in the automated processing from social media sources. Additionally, the research team will develop and release new annotated data that will enable new research in this direction. Furthermore, this project will address broadening participation in computer science by supporting graduate and undergraduate students, several of them from underrepresented groups in Computer Science.The underlying premise of this project is that a tighter coupling of representation learning with linguistic and domain knowledge will allow the models to learn the tasks by distilling all relevant linguistic abstractions in each single text, without requiring prohibitively large amounts of labeled data, as is typically the case in end-to-end deep-learning models. This award will design robust approaches for sequence labeling tasks that can analyze social media data with a two-pronged approach. First, the research team will study the challenges imposed by social media data and their correlation to prediction performance. Then the investigators will design new model architectures for sequence labeling tasks where domain and linguistic knowledge supervise the learning process. The evaluation of the proposed models will include data from different social media sources.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.

在过去的十年中，社交媒体平台对人们沟通方式的影响越来越大;这些平台现在被认为是人们广泛使用的重要沟通工具，用于分享信息，也用于了解任何主题的最新事件。因此，由用户、公司、媒体和政治实体产生的信息在这些平台上运行，与了解当前事件、行为等非常相关，这些数据的自动化提炼具有很大的实用价值。目前的文本处理技术无法在社交媒体数据上准确地执行信息提取，因为这些复杂的算法已经在高度编辑的英语文本上进行了训练，主题范围很窄，例如新闻通讯数据。相比之下，社交媒体数据具有流畅的语法，非常大的词汇量，无限的主题，并且包括经常混合在同一文本中的多种语言。该项目解决了来自社交媒体来源的自动化处理所涉及的许多挑战。此外，研究团队将开发和发布新的注释数据，以实现这一方向的新研究。此外，该项目将通过支持研究生和本科生来扩大计算机科学的参与，其中一些学生来自计算机科学中代表性不足的群体。该项目的基本前提是，表征学习与语言和领域知识的更紧密耦合将允许模型通过提取每个文本中所有相关的语言抽象来学习任务，而不需要像端到端深度学习模型中的典型情况那样大量的标记数据。该奖项将为序列标记任务设计强大的方法，这些任务可以通过双管齐下的方法分析社交媒体数据。首先，研究团队将研究社交媒体数据带来的挑战及其与预测性能的相关性。然后，研究人员将为序列标记任务设计新的模型架构，其中领域和语言知识监督学习过程。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知识价值和更广泛的影响审查标准进行评估来支持。