RI: Small: Semantics-Based, Weakly-Supervised Coreference Resolution

RI：小：基于语义的弱监督共指解析

• 批准号: 1219142
• 负责人: Vincent Ng
• 金额: $ 34.74万
• 依托单位: University of Texas at Dallas
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1219142&HistoricalAwards=false
• 关键词: RI; Small; Semantics; Based; Weakly

This research seeks to push the frontiers of coreference resolution research via achieving two objectives. First, it addresses the Winograd Schema Challenge by examining a class of difficult-to-resolve anaphors whose resolution requires commonsense knowledge involving the roles played by the participants in an event and their causal relationships. It adopts a deep text-understanding approach to this problem. Specifically, it ventures into unexplored areas of coreference research, including the use of script-like knowledge and sentiment analysis, as well as an examination of the role of discourse connectives. Second, it enables the acquisition of coreference resolvers for a substantially larger number of natural languages and domains than is currently possible. One of the major obstacles to the deployment of coreference technologies across a large number of languages and domains is the high cost associated with coreference-annotating data in a language and domain. It investigates two cost-effective approaches to data annotation, one involving translation-based projection and the other bootstrapping.As coreference is an enabling technology for many traditional and emerging text-processing applications, the project has the potential to improve these applications. Through the construction of a multi-lingual, multi-domain coreference resolver and the availability of annotated data produced in the course of this investigation, the project may stimulate research in under-studied languages and domains by a broader community of researchers. Equally importantly, the use of commonsense knowledge in the resolver mimics the human coreference resolution process, bringing artificial intelligence researchers one step closer to building an intelligent agent that can truly understand natural language.

本研究旨在通过实现两个目标来推动共指消解研究的前沿。首先，它解决了Winograd模式的挑战，通过检查一类难以解决的回指，其决议需要常识知识，涉及的参与者在一个事件中所扮演的角色和他们的因果关系。它采用了一种深入的文本理解方法来解决这个问题。具体来说，它冒险进入未开发的领域的共指研究，包括使用脚本一样的知识和情感分析，以及话语连接词的作用的检查。第二，它使收购的共指解析器的自然语言和域的数量远远大于目前可能的。跨大量语言和领域部署共指技术的主要障碍之一是与在语言和领域中注释数据的共指相关的高成本。它研究了两种成本效益高的数据标注方法，一种涉及基于投影的投影，另一种涉及自举，由于共指是许多传统和新兴文本处理应用的一种使能技术，该项目有可能改善这些应用。通过构建一个多语言、多领域的共指消解器，并提供本调查过程中产生的注释数据，该项目可能会刺激更广泛的研究人员对研究不足的语言和领域进行研究。同样重要的是，在解析器中使用常识知识模仿了人类的共指消解过程，使人工智能研究人员更接近于构建一个能够真正理解自然语言的智能代理。