RI: Small: Collaborative Research: Research Leading to Comprehensive Guidelines for Discourse Relation Annotation

RI：小型：协作研究：研究导致话语关系注释的综合指南

• 批准号: 1421067
• 负责人: Rashmi Prasad
• 金额: $ 20万
• 依托单位: University of Wisconsin-Milwaukee
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1421067&HistoricalAwards=false
• 关键词: RI; Small; Collaborative; Research; Leading

Machine Translation, automated question answering, dialogue systems -- the many useful, emerging language technologies -- depend on recognizing patterns in text. Right now, the only patterns that can dependably be recognized are very local, no bigger than a sentence clause. Enabling patterns to be recognized across clauses in a text by identifying what links them and what the link conveys was the goal of the NSF-supported Penn Discourse TreeBank (PDTB), a nearly 1-million word text resource labelled with text-linking devices ("discourse connectives" and adjacency), the spans of text they link, and what the link conveys. In the five years since the release of the PDTB, computational linguistics researchers from around the world have used the format it pioneered,to develop similar resources for other languages and to use these resources for recognizing larger patterns in text. The current PDTB, however, lacks the full range of explicit and implicit text-linking devices in English and what they convey; the information which is badly needed by many forward-looking language technology applications. The goal of this project is to conduct research with the purpose to enrich the PDTB with these additional devices and to develop ways for authoritatively annotating other texts with similar information, but with less manual effort, as a basis for extending the range of texts whose larger, cross-clausal patterns can be recognized automatically.This project is a response to calls (from both the language technology and computational psycholinguistics communities) for increased coverage and continuity of discourse relation annotation, both across and within the sentences of a text. To ensure a systematic annotation scheme grounded in evidence, the project starts by addressing some foundational questions about the properties of additional linguistic signals of discourse relations and how to capture these properties consistently and completely through manual annotation. From this follows systematic, evidence-grounded annotation of Entity Relations; constructions (other than discourse connectives) that reliably signal discourse relations; implicit intra-sentential discourse relations (building on PropBank annotation of the Penn TreeBank, and concurrent discourse relations (where implicit relations hold in addition to ones signaled explicitly). The project also explores the use of crowd-sourcing to support sub-tasks in discourse relation annotation that would lead to a reduction in the manual effort needed for expert annotation of other corpora, or enable large-scale experiments on aspects of human understanding of discourse relations. As with the Penn Discourse TreeBank 2.0, the enhanced corpus resulting from the project will be disseminated by the Linguistic Data Consortium (LDC), a well-established institution for world-wide distribution of language resources.

机器翻译、自动问答、对话系统--许多有用的新兴语言技术--依赖于识别文本中的模式。目前，唯一可以可靠识别的模式是非常局部的，不超过一个句子的从句。NSF支持的宾夕法尼亚语篇树库(PDTB)的目标是通过识别语篇中的从句之间的链接以及链接所传达的信息来识别模式。PDTB是一个近100万字的文本资源，使用文本链接设备(语篇连接词和邻接)、它们链接的文本跨度以及链接所传达的内容来标记。在PDTB发布后的五年里，来自世界各地的计算语言学研究人员使用了它开创的格式，为其他语言开发了类似的资源，并利用这些资源识别文本中的较大模式。然而，目前的PDTB缺乏全面的英语显性和隐性文本链接手段及其所传达的信息；这些信息是许多前瞻性语言技术应用程序迫切需要的。这个项目的目的是进行研究，目的是用这些额外的工具来丰富PDTB，并开发方法来权威地注释其他具有类似信息的文本，但需要更少的人工工作，作为扩大其更大的跨从句模式可以被自动识别的文本范围的基础。本项目是对(来自语言技术和计算心理语言学界的)要求增加语篇关系注释的覆盖率和连续性的呼声的响应，无论是在句子之间还是在句子内。为了确保系统的注释方案以证据为基础，该项目首先解决了一些基本问题，即话语关系的额外语言符号的属性，以及如何通过手动注释一致和完整地捕获这些属性。在此基础上出现了系统的、基于证据的实体关系注释；可靠地表示话语关系的构式(除了话语连接词)；隐含的句内话语关系(建立在宾夕法尼亚树库的PropBank注释基础上)，以及并发话语关系(其中，除了明确表示的关系外，还存在隐含关系)。该项目还探索了使用众包来支持话语关系标注中的子任务，这将导致减少其他语料库的专家标注所需的人工工作量，或使大规模实验能够在人类对话语关系的理解方面进行。与宾夕法尼亚大学话语树库2.0一样，该项目产生的增强语料库将由语言数据联盟(LDC)传播，这是一个在世界范围内分配语言资源的著名机构。