Improving, Extending and Leveraging Discourse Parsing

改进、扩展和利用语篇解析

• 批准号: RGPIN-2017-04446
• 负责人: Carenini, Giuseppe
• 金额: $ 1.89万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=711049
• 关键词: Improving; Extending; Leveraging; Discourse; Parsing

The sentences in a document are related to each other in order to express complex ideas. For instance, two sentences can be in a contrast relation is they present opposite ideas, or in an elaboration relation is one sentence is presenting more details on the idea expressed by the other. A discourse parser is a software program that given a document as input is able to extract all the relations between its sentences. The output of discourse parsing can then be leveraged to support many other useful tasks, like creating a summary of the document or determining the opinions expressed by the document. The main goal of the proposed project is to improve current discourse parsing technology in several ways. First, we aim to boost the accuracy and the speed of current parsers by applying novel techniques from artificial intelligence and machine learning. Secondly, we will study how other text processing tasks, like summarization and text mining, can maximally benefit from the output of our more accurate and faster discourse parsers. Finally, we will extend discourse parsing beyond text, to deal with the many documents, ranging from newspaper articles to scientific publications, in which text is combined with visual material (i.e., multimodal documents). In addition to scientific impact, we expect this research to have real and significant economic and social benefits for Canada. There is a rapidly expanding demand for applications that are designed to help users understand and manipulate complex bodies of textual and multimodal documents, both in the workplace and for personal use. For instance, journalists often need to analyze many documents to check facts and discover stories, while citizens may benefit from summaries of news from different sources to build more informed opinions about current events. Similarly, in the healthcare sector, summaries of patient histories and relevant medical literature could be very useful to doctors, while patients may need support in exploring on-line discussions about their condition. Finally, consider the business domain, where Canadian companies could better understand their customers and develop better products by mining online reviews. Conversely, consumers, by accessing the same information, could make more informed purchases. The proposed research has great potential to fuel these and many other applications, by boosting discourse parsing performance, extending its applicability to documents including visualizations and by turning these improvements into robust and versatile text processing technologies that can benefit both Canadian companies and citizens.

文档中的句子是相互关联的，以表达复杂的思想。例如，两个句子可以是对比关系，它们表达的是相反的想法，或者在阐述关系中，一个句子表示另一个句子所表达的想法的更多细节。语篇分析器是一种软件程序，它给出一个文档作为输入，能够提取它的句子之间的所有关系。然后，可以利用话语分析的输出来支持许多其他有用的任务，例如创建文档摘要或确定文档所表达的观点。该项目的主要目标是在几个方面改进现有的语篇分析技术。首先，我们的目标是通过应用人工智能和机器学习的新技术来提高现有解析器的准确性和速度。其次，我们将研究其他文本处理任务，如摘要和文本挖掘，如何最大限度地受益于我们更准确、更快的语篇分析器的输出。最后，我们将把语篇分析扩展到文本之外，以处理从报纸文章到科学出版物的许多文档，在这些文档中，文本与视觉材料(即多模式文档)相结合。除了科学影响，我们预计这项研究将为加拿大带来真正和重大的经济和社会效益。在工作场所和个人使用中，对旨在帮助用户理解和处理复杂的文本和多模式文档的应用程序的需求迅速增长。例如，记者经常需要分析许多文件来核实事实和发现故事，而公民可能会受益于不同来源的新闻摘要，以建立对时事的更有见地的意见。同样，在医疗保健领域，病历摘要和相关医学文献可能对医生非常有用，而患者可能需要支持，以探索关于其病情的在线讨论。最后，以商业领域为例，加拿大公司可以通过挖掘在线评论来更好地了解客户并开发更好的产品。相反，消费者通过获取相同的信息，可以进行更明智的购买。拟议的研究具有极大的潜力来推动这些和许多其他应用，通过提高语篇解析性能，将其适用于包括可视化在内的文档，并将这些改进转化为稳健和通用的文本处理技术，从而使加拿大公司和公民都受益。