EAGER: Joint Learning for Knowledge-Rich Coreference Resolution
EAGER: Joint Learning for Knowledge-Rich Coreference Resolution
批准号:
1147644
负责人:
Vincent Ng
金额:
$13.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2011
资助国家:
美国
项目状态:
已结题
起止时间:
2011-08-15 至 2014-01-31
中文摘要
这项探索性研究的早期资助旨在调查知识丰富、联合学习的共指消解方法的可行性,最终目标是推进共指消解的最先进水平。鉴于词汇语义和语篇研究的最新进展,以及大规模词汇数据库的发展,这项资助的首要目标是调查现有的语言技术是否足够成熟,能够从结构化和非结构化数据中准确提取语义、语篇和世界知识,以便在使用这些知识时可以显着改进基于学习的共指系统。第一个目标的假设是使用管道系统架构,在共指解析之前计算来自各种来源的复杂语言信息。虽然管道架构在共指研究中广泛使用,但上游组件产生的错误可能会传播到共指组件并对其性能产生不利影响。为了解决这个问题,这笔赠款的第二个目标是探索一种以联合方式学习管道中多个任务的方法。虽然大多数关于语言处理联合学习的研究都集中在两个任务上,但这项工作试图通过同时学习语义、话语和信息提取方面的大量任务,将联合学习所涉及的挑战提升到一个新的水平,这些任务都可以从它们之间的相互作用以及学习过程中的共指中受益。
英文摘要
This Early Grant for Exploratory Research seeks to investigate the viability of a knowledge-rich, joint-learning approach to coreference resolution, with the ultimate goal of advancing the state of the art in coreference resolution. Given recent advances in research on lexical semantics and discourse, and the development of large-scale lexical databases, the first objective of this grant is to investigate whether existing language technologies are mature enough to accurately extract semantic, discourse, and world knowledge from structured and unstructured data so that learning-based coreference systems can be significantly improved when such knowledge is employed.An assumption underlying the first objective is the use of a pipeline system architecture, where sophisticated linguistic information from various sources is computed prior to coreference resolution. While a pipeline architecture is popularly-used in coreference research, the errors made by the upstream components may propagate to the coreference component and adversely affect its performance. To address this problem, the second objective of this grant is to explore an approach in which multiple tasks in the pipeline are learned in a joint fashion. While most research on joint learning for language processing focuses on two tasks, this work seeks to take the challenge involved in joint learning to the next level by simultaneously learning a large number of tasks in semantics, discourse, and information extraction, which can all benefit from their interactions with each other and with coreference in the learning process.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
Second Workshop on Coreference Resolution Beyond OntoNotes
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批准号:1734696
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项目类别:Standard Grant
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资助金额:$0.8万
-
财政年份:2017
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负责人:Vincent Ng
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依托单位:
EMNLP 2016 Student Scholarship Program
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批准号:1653286
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项目类别:Standard Grant
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资助金额:$0.75万
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财政年份:2016
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负责人:Vincent Ng
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依托单位:
RI: Small: Fast, Scalable Joint Inference for NLP using Markov Logic
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批准号:1528037
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项目类别:Standard Grant
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资助金额:$36.03万
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财政年份:2015
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负责人:Vincent Ng
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依托单位:
RI: Small: Semantics-Based, Weakly-Supervised Coreference Resolution
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批准号:1219142
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项目类别:Continuing Grant
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资助金额:$34.74万
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财政年份:2012
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负责人:Vincent Ng
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依托单位:
RI-Small: Improving Machine Learning Approaches to Coreference Resolution
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批准号:0812261
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项目类别:Continuing Grant
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资助金额:$25.0万
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财政年份:2008
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负责人:Vincent Ng
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依托单位:
国内基金
海外基金
基于双稳健共享参数Joint模型的脑卒中早期关键风险因素推断研究
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批准号:81803337
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项目类别:青年科学基金项目
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资助金额:21.0万元
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批准年份:2018
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负责人:石福艳
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依托单位: