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

机器翻译、自动问答、对话系统--许多有用的新兴语言技术--都依赖于识别文本中的模式。现在，唯一能被识别的模式是非常局部的，不超过一个句子从句。通过识别什么链接它们以及链接传达的内容来识别文本中跨子句的模式是NSF支持的Penn Discourse TreeBank（PDTB）的目标，这是一个近100万字的文本资源，标有文本链接设备（“话语连接词”和邻接），它们链接的文本跨度以及链接传达的内容。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它开创的格式为其他语言开发了类似的资源，并使用这些资源识别文本中的较大模式。然而，目前的PDTB缺乏英语中所有的显式和隐式文本链接设备及其所传达的信息;许多前瞻性语言技术应用程序迫切需要的信息。该项目的目标是进行研究，目的是用这些额外的设备来丰富PDTB，并开发出作者注释具有类似信息的其他文本的方法，但人工工作量较少，作为扩展文本范围的基础，跨从句模式可以被自动识别。这个项目是对呼叫的响应（来自语言技术和计算心理语言学社区）用于增加跨文本的句子和在文本的句子内的话语关系注释的覆盖范围和连续性。为了确保系统的注释方案以证据为基础，该项目首先解决一些基本问题，这些问题涉及话语关系的附加语言信号的属性，以及如何通过手动注释一致和完整地捕获这些属性。从这一点可以得出系统的、基于证据的实体关系注释;可靠地表示话语关系的结构（而不是话语连接词）;隐含的内部话语关系（建立在Penn TreeBank的PropBank注释上），以及并发的话语关系（除了明确表示的关系之外，隐含的关系也成立）。该项目还探讨了使用众包来支持话语关系注释中的子任务，这将导致减少专家注释其他语料库所需的人工工作，或使人类理解话语关系方面的大规模实验成为可能。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合会（LDC）传播，这是一个在世界范围内分发语言资源的成熟机构。

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.