CAREER: Cross-Document Cross-Lingual Event Extraction and Tracking

职业：跨文档跨语言事件提取和跟踪

• 批准号: 1523198
• 负责人: Heng Ji
• 金额: $ 22.8万
• 依托单位: Rensselaer Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-06-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1523198&HistoricalAwards=false
• 关键词: CAREER; Cross; Document; Lingual; Event

The goal of this research project is advance the Information Extraction (IE) paradigm beyond "slot filling", and achieve more accurate, salient, complete, concise and coherent extraction results by exploiting dynamic background knowledge and cross-document cross-lingual event ranking and tracking. The approach consists of cross-document inference, unknown implicit event time prediction and reasoning, cross-document entity coreference resolution with global contexts, centroid entity detection, event attribute extraction and graph-based clustering algorithms for redundancy and contradiction detection, automatic new event clustering and active learning, abstractive summary generation based on extraction results, name translations with comparable corpora and cross-lingual co-training.The experimental research is integrated with educational activities, including project-related curriculum development. The project involves PhD students as well as undergraduate students, engages non-Computer Science undergraduate students in utility evaluation and corpus annotation, and attracts elementary school and high school students by tutorials, regular research seminars and an extensive summer workshop. The results of this project will also have a benefit in E-Science and E-Learning by extracting and tracking the related knowledge from scientific literature and learning materials used in elementary schools and high schools.Project results, including open source software, task definition guidelines, annotated corpora, scoring metrics will be disseminated via project Web site (http://nlp.cs.rpi.edu/blendeet.html).

本课题的研究目标是将信息抽取的研究范式从“槽填充”的角度加以改进，通过利用动态背景知识和跨文档跨语言事件的排序和跟踪，实现更加准确、显著、完整、简洁和连贯的抽取结果。该方法包括跨文档推理、未知隐式事件时间预测和推理、全局上下文下的跨文档实体共指消解、质心实体检测、事件属性提取和基于图的聚类算法（用于冗余和矛盾检测）、新事件自动聚类和主动学习、基于提取结果的摘要生成名称翻译与可比语料库和跨语言共同培训。实验研究与教育活动相结合，包括项目相关的课程开发。该项目涉及博士生以及本科生，从事非计算机科学本科生的效用评估和语料库注释，并吸引小学和高中学生的辅导，定期研究研讨会和广泛的夏季研讨会。本项目的成果还将通过从中小学使用的科学文献和学习材料中提取和跟踪相关知识而有益于电子科学和电子学习，项目成果，包括开放源码软件、任务定义指南、注释语料库、评分标准将通过项目网站（http：//nlp.cs.rpi.edu/blendeet.html）传播。