Computational Aspects of Discourse Analysis

话语分析的计算方面

• 批准号: RGPIN-2014-05540
• 负责人: Kosseim, Leila
• 金额: $ 2.33万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=635060
• 关键词: Computational; Aspects; Discourse; Analysis

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 OBJECTIVEDiscourse 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 APPROACHThis 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 WORKWith 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.

动机“编写NSERC DG应用程序需要时间.简两个月就写好了。在连贯的文本中，文本单位不是孤立地理解的，而是通过可能有或可能没有明确标记的话语关系相互联系起来的。简在两个月内写了她的申请，这一事实“说明”写一个NSERC DG申请是漫长的！语篇分析的研究试图建立这种关系的模型，这使我们能够解释文本并理解其单位的交际目的。反过来，这对许多自然语言处理应用程序非常有用，例如自动摘要，文本简化等。长期连贯语篇连贯是指语篇单位之间的逻辑联系（如“说明”、“结果”、“目的”等）。虽然近年来已经做了很多工作，但计算语篇分析仍处于起步阶段，许多重要的开放性问题仍然存在。本研究计划的长期目标是探索自动话语分析的计算方面。科学方法本研究将探讨三个具体问题：1）体裁对语篇标注的影响：在这里，我们将探讨语篇体裁（如政治，程序，科学，.）影响话语关系的运用。跨领域和体裁的关系在语言实现上存在显著差异，需要捕捉和建模。我们将讨论的问题包括：语篇体裁对关系的使用和话语标记语的选择有什么影响？全球话语结构与地方关系之间的互动是什么？我们能否在特定的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，上下文，句法和语义信息）和文本类型之间的相关性，这将被用来定制话语标记到特定的类型。2）跨语言的无监督话语标记：在这里，我们将研究如何使用和语言实现的关系在不同的语言。我们将对英语和法语中的语篇关系进行跨语言比较，并集中讨论两个问题：语篇关系的使用是否独立于语言？语篇关系如何在不同语言之间保持一致？这项工作将产生一个大型的跨语言资源，我们将提供给研究社区，并将用于从另一种语言的其他分析器中诱导一种语言的话语分析器。3）使用话语分析进行文本简化：在这里，我们将探讨如何使用话语分析来自动改进简化文本，使其能够被更广泛的受众所访问，无论他们的语言技能如何。特别是，我们将解决两个问题：我们可以实现文本简化信号隐含的关系更明确？我们可以通过修剪信息量较少的文本跨度来实现文本简化吗？工作的意义随着自然语言处理研究的最新发展，话语分析的计算方面的工作现在已经成为可能，并且在研究界越来越感兴趣。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。具体而言，我们的工作可以使加拿大语言行业的所有部门受益：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语言行业。