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Panda Alert Research Proposal

熊猫警报研究计划

基本信息

批准号：
1649558
负责人：
金额：
--
依托单位：
University of Cambridge
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2015
资助国家：
英国
起止时间：
2015 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-1649558
关键词：
Panda Alert Research Proposal

项目摘要

The Panda Alert System aims to improve the state of the art in early disease outbreakdetection by incorporating linguistic features into the language models which werepreviously not known/seen to indicate any risk to human health. The project willdevelop a fully functioning, real-time alerting and mapping system.This kind of research project assumes significant knowledge/use of softwareengineering methodologies combined with novel research into unsupervised orsemi-supervised NLP models. We propose a layered, modular architecturedetachable from the core NLP engine, which should demonstrate a universaldetection capability easily transferable to new domains.The most likely approach would be a semi-supervised, bootstrapping model, whichlearns from a small amount of training data to generalise over the unknown domain.Harnessing the latest NLP machine learning methods such as neural networks (deeplearning), the model analyses news reports to flag any risks to public health. All toolsand data sets from this research will be open sourced and available for downloadfrom public repositories such as GitHub or Google Code.It is vital for the future reusability of the core NLP engine to be domain-agnostic sothat it can be extended and adapted via a clear programming interface to new eventdetection tasks. The real-time analysis system will have to handle hundreds ofthousands of news articles and social media items per day in multiple languages.The research part of the project will take the state of the art in NLP and AI andextend it to establish novel methods of NLP general discourse pattern detection inorder to analyse statements for known and unknown features potentially indicativeof the fact-bearing target knowledge. The new technique should be capable ofhandling the identification and reporting of other target knowledge/facts from casualonline user/news activity with minimum or no training.In year one, I shall be mainly focusing on toponym resolution and geoparsing. Thesetechniques comprise NLP techniques for identification of place names in text andtheir subsequent resolution to geographical coordinates. This task is easy for humansto perform, however for a machine to tell which London (UK, Canada, etc.) wasmentioned in a text is a non-trivial job. I aim to improve on the existing baselines byresearching a novel method of geoparsing. We aim to publish the findings next yearat a respected conference.

熊猫预警系统旨在通过将语言特征纳入语言模型来改善早期疾病爆发检测的最新技术水平，这些语言模型以前不知道/看到对人类健康有任何风险。该项目将开发一个功能齐全的实时警报和映射系统。这种研究项目假设软件工程方法的重要知识/使用与无监督或半监督NLP模型的新研究相结合。我们提出了一个分层的，模块化的架构，可从核心NLP引擎中分离出来，它应该展示出一个通用的检测能力，可以很容易地转移到新的领域。最有可能的方法是一个半监督的，自举模型，它从少量的训练数据中学习，以推广到未知的领域。利用最新的NLP机器学习方法，如神经网络，该模型分析新闻报道，以标记任何对公共健康的风险。本研究的所有工具和数据集都将开源，并可从GitHub或Google Code等公共存储库下载。核心NLP引擎的未来可重用性至关重要，因为它与领域无关，因此可以通过清晰的编程接口进行扩展和调整，以适应新的事件检测任务。实时分析系统每天将处理数十万篇多语言的新闻文章和社交媒体项目。该项目的研究部分将采用NLP和AI的最新技术，并将其扩展到建立NLP一般话语模式检测的新方法，以分析可能指示事实承载目标知识的已知和未知特征。新的技术应该能够处理识别和其他目标知识/事实的报告，从网上用户/新闻活动与最低限度或没有培训。在第一年，我将主要集中在地名解析和地理解析。这些技术包括NLP技术，用于识别文本中的地名及其随后的地理坐标解析。这项任务对人类来说很容易完成，但是对机器来说，要分辨哪个伦敦（英国，加拿大等）在一篇文章中提到的是一个不平凡的工作。我的目标是通过研究一种新的地理解析方法来改进现有的基线。我们的目标是在明年的一个受人尊敬的会议上发表这些发现。