Computational Aspects of Discourse Analysis

动机*“编写NSERC DG应用程序需要时间……简两个月就写完了。”*在连贯的文本中，文本单位不是孤立地理解的，而是通过话语关系相互联系起来的，这些话语关系可能被明确标记，也可能不被明确标记。事实上，Jane在两个月内完成了她的申请，“说明”编写NSERC DG申请是很长的！语篇分析的研究试图对这种关系进行建模，从而使我们能够理解语篇，理解语篇中各单元的交际目的。这反过来又对许多自然语言处理应用程序很有用，例如自动摘要、文本简化……**语篇连贯是指语篇单位之间的逻辑联系（例如，“说明”、“结果”、“目的”等）。虽然近年来已经做了很多工作，但计算语篇分析仍然处于起步阶段，仍然存在许多重要的开放性问题。本研究计划的长期目标是探索自动语篇分析的计算方面。**科学方法*本研究将探讨3个具体问题：**1)体裁对语篇标注影响的探索：在这里，我们将探讨语篇体裁（如政治、程序、科学等）如何影响语篇关系的使用。跨领域和跨体裁关系的语言实现存在显著差异，需要捕获和建模。我们将讨论的问题类型包括：文本类型对关系的使用和话语标记的选择有什么影响？全球话语结构与地方关系之间的互动是什么？我们能否在特定文本体裁中识别出地方关系的刻板模式？这项工作将识别和测量语言特征（词汇、语境、句法和语义信息）与文本类型之间的相关性，这些特征将用于为特定类型定制话语标记器。**2)跨语言的无监督话语标注：在这里，我们将研究关系的使用和语言实现在不同语言之间的差异。我们将对英语和法语的语篇关系进行跨语言比较，并关注两个问题：语篇关系的使用是否独立于语言？话语关系在不同语言间是如何对齐的？这项工作将产生一个大型的跨语言资源，我们将向研究界提供，并将用于从另一种语言的其他解析器中归纳出一种语言的话语解析器。**3)使用语篇分析进行文本简化：在这里，我们将探讨如何使用语篇分析来自动改进简化文本，使更广泛的受众能够访问这些文本，而不管他们的语言技能如何。特别是，我们将解决两个问题：我们能否通过更明确地表示隐含关系来实现文本简化？我们是否可以通过修剪信息较少的文本跨度来实现文本简化？随着自然语言处理研究的当前状态，话语分析的计算方面的工作现在已经成为可能，并且在研究界引起了越来越多的兴趣。解决这个问题将使我们超越理解文本的字面意义，而是解释其“深层”意义或交际意图。具体来说，我们的工作可以为加拿大语言行业的所有部门带来好处：语音处理、机器辅助翻译、内容管理、语言电子学习……有了这些潜在的应用，我相信我们的工作可以很容易地转移到加拿大的语言产业。

MOTIVATION*"Writing an NSERC DG application takes time... Jane wrote hers in two months." *In a coherent text, textual units are not understood in isolation but in relation with each other through discourse relations that may or may not be explicitly marked. The fact that Jane wrote her application in two months, "illustrates" that writing a NSERC DG application is long! Research on discourse analysis tries to model such relations, which allows us to interpret the text and understand the communicative purpose of its units. This, in turn, is useful for many Natural Language Processing applications such as automatic summarization, text simplification...**LONG TERM OBJECTIVE*Discourse coherence refers to the logical connections between textual units (for example, "illustration", "result", "purpose"...). Although, much work has been done in recent years, computational discourse analysis is still in its infancy and many important open questions still remain. The long term objective of this research program is to explore computational aspects of automatic discourse analysis. **SCIENTIFIC APPROACH*This research will explore 3 specific questions: **1) The exploration of the effect of genre on discourse tagging: Here, we will explore how the textual genre (e.g. political, procedural, scientific,...) affects the use of discourse relations. There are significant differences in the linguistic realization of relations across domains and genres that need to be captured and modeled. The types of questions we will address include: What is the influence of the textual genre on the usage of relations and choice of discourse markers? What is the interaction between global discourse structures and local relations? Can we identify stereotypical patterns of local relations in particular textual genres? This work will identify and measure the correlation between linguistic features (lexical, contextual, syntactic and semantic information) and textual genres which will be used to tailor discourse taggers to a specific genre.**2) Unsupervised discourse tagging across languages: Here we will investigate how the usage and linguistic realization of relations vary across languages. We will perform a cross-lingual comparison of discourse relations in English and French, and focus on two questions: Is the usage of discourse relations language-independent? How do discourse relations align across languages? This work will result in a large cross-lingual resource which we will make available to the research community and will be used to induce discourse parsers in one language from other parsers in another language.**3) The use of discourse analysis for text simplification: Here we will explore how discourse analysis can be used to improve simplify texts automatically to make them accessible to a wider audience regardless of their language skills. In particular, we will address two questions: Can we achieve text simplification by signaling implicit relations more explicitly? Can we achieve text simplification by pruning less informative text spans?**SIGNIFICANCE OF THE WORK*With the current state of the art in Natural Language Processing research, work on computational aspects of discourse analysis has now become possible and is growing much interest in the research community. Addressing this issue will allow us to go beyond understanding the literal meaning of a text, but interpret its "deep" meaning or communicative intention. Concretely, our work can be beneficial to all sectors of the Canadian language industry: in speech processing, machine-aided translation, content management, language e-learning... With all these potential applications, I am confident that our work can easily be transferred to the Canadian language industry.