Between the Lines - Knowledge-enhanced Argument Analysis in a Formal Argumentation Reasoning System

字里行间 - 形式论证推理系统中的知识增强论证分析

• 批准号: 375654996
• 负责人: Professorin Dr. Anette Frank
• 金额: --
• 依托单位: Institut für Computerlinguistik
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2017
• 资助国家: 德国
• 起止时间: 2016-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/375654996?language=en
• 关键词: Between; Lines; Knowledge; enhanced; Argument

Argumentation is widely used in political discourse and general communication. Arguments vary in strength and quality, and what is explicitly said reflects only part of the knowledge and reasoning that underlies an argument, as understood by humans. This project will investigate methods to computationally analyze and validate arguments in order to i) complement the overtly expressed argument with automatically acquired knowledge that provides missing explanatory links, to ii) construct a formal, knowledge-enhanced analysis of the argument, and on this basis, iii) establish and verify the extended argument structure using a combination of machine learning and formal reasoning. We aim to advance current methods in argument analysis by developing a knowledge-enhanced formal argument reasoning system that analyses argumentative texts semantically. We achieve this by analyzing the semantic coherence between statements in an argument. We will link entities and concepts in the given statements to knowledge bases and learn to reconstruct implicitly understood background knowledge that enhances the argument's semantic coherence. As a result of this process we will obtain an abstract argumentation knowledge graph, constructed over explicit argumentative text and enriched with relevant implicit knowledge obtained from existing knowledge bases or harvested from textual sources. We apply supervised machine learning to detect abstract patterns in the linked knowledge, in order to determine and score relevant (types of) connecting knowledge. A formal reasoning process will then jointly establish the formal argument structure and determine the strength of the argument, based on the semantic coherence of each potential edge in the argumentation graph based on the type, the amount and the connectivity of the explicit statements and the added implicit knowledge.The outcome will be a semantically enriched formal representation of arguments linked to extensible knowledge sources: structured knowledge bases that are dynamically enriched from textual sources with various kinds of background and domain-specific information. We will employ linked knowledge repositories of factual knowledge (eg. DBpedia, or Gene Ontology), linguistic ontologies (WordNet), and large repositories of common sense knowledge (ConceptNet, OpenCyc) in conjunction with automatically harvested background knowledge, from relevant text or web corpora. We apply our methods to argumentation in general and special domains in different genres, and perform both component evaluations and benchmarking against existing systems.Within the SPP Robust Argumentation Machines our project focuses on Validation: knowledge-enhanced analysis will make the underlying logics and implicit assumptions of arguments explicit. The project will contribute to community actions, shared tasks and evaluation efforts by providing novel data sets on implied knowledge in arguments and an evaluation platform, among others.

议论文广泛应用于政治语篇和一般交际中。论证在强度和质量上各不相同，明确说出来的只是作为论证基础的知识和推理的一部分，就像人类所理解的那样。该项目将研究计算分析和验证论点的方法，以便i）用自动获取的知识补充公开表达的论点，提供缺失的解释性链接，ii）构建一个正式的，知识增强的论点分析，并在此基础上，iii）使用机器学习和形式推理的组合建立和验证扩展的论点结构。我们的目标是通过开发一个知识增强的形式化论证推理系统，从语义上分析论证文本，从而推进当前的论证分析方法。我们通过分析论点中陈述之间的语义连贯性来实现这一点。我们将把给定陈述中的实体和概念与知识库联系起来，并学习重建隐含理解的背景知识，以增强论点的语义连贯性。作为这个过程的结果，我们将获得一个抽象的论证知识图，构建在明确的论证文本和丰富的相关隐含的知识，从现有的知识库或收获的文本来源。我们应用监督机器学习来检测链接知识中的抽象模式，以确定相关的连接知识并对其进行评分。一个正式的推理过程将共同建立正式的论点结构，并确定论点的强度，基于论证图中每个潜在边的语义一致性，基于显式陈述和添加的隐式知识的类型，数量和连接性。结果将是链接到可扩展知识源的论点的语义丰富的正式表示：结构化知识库，其从具有各种背景和领域特定信息的文本源动态地丰富。我们将使用事实知识的链接知识库（例如。DBpedia或Gene Ontology）、语言本体（WordNet）和大型常识知识库（ConceptNet、OpenCyc），以及从相关文本或网络语料库自动获取的背景知识。 我们将我们的方法应用于不同流派的一般和特殊领域的论证，并对现有系统进行组件评估和基准测试。在SPP鲁棒论证机器中，我们的项目侧重于验证：知识增强分析将使潜在的逻辑和参数的隐含假设明确。该项目将通过提供关于论点中隐含知识的新数据集和一个评价平台等，促进社区行动、共同任务和评价工作。