Open Argument Mining

开放论点挖掘

• 批准号: 413534432
• 负责人: Professorin Dr. Iryna Gurevych
• 金额: --
• 依托单位: Fachgebiet Ubiquitäre Wissensverarbeitung
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2018
• 资助国家: 德国
• 起止时间: 2017-12-31 至 2023-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/413534432?language=en
• 关键词: Open; Argument; Mining

Open debates include so many arguments that sound decision making exceeds cognitive capabilities of the interested public or responsible experts. New arguments are continuously contributed (challenge C1), are oftenincomplete (C2), and knowledge about common facts or previous arguments is needed to understand them (C3).This project aims at investigating computational methods that i) continuously improve their capability to recognize arguments in ongoing debates, ii) align incomplete arguments with previous arguments and enrichthem with automatically acquired background knowledge, and iii) constantly extend semantic knowledge bases with information required to understand arguments.We achieve this by combining and advancing current state-of-the-art algorithms from the two research fields argument mining and knowledge graph construction. To deal with concept drifts in ongoing debates, we aim to advance argument mining methods with a knowledge-aware lifelong learning approach. We will investigate novel neural architectures for learning topic invariant argument features and the relation between arguments and debate topics, inject semantic knowledge into the neural network using knowledge graph embeddings and leverage self-training to continuously extend the training data. To cope with incomplete arguments, the retrieved arguments will be aligned with known arguments and enriched with background knowledge. We will link the entities of arguments to background knowledge by combining link discovery and keyword search. This linked background knowledge will be incorporated into incremental clustering methods for grouping similar arguments into argument clusters. Argumentative support and attack relations between these argument clusters will be determined using supervised learning. We aim to automatically acquire the required background knowledge by combining contemporary semantic knowledge bases containing encyclopedic and commonsense knowledge (Babelnet and ConceptNet) and focused knowledge extraction from unstructured Web corpora (Common Crawl). To integrate this background knowledge into machine learning models, we are going to adopt existing knowledge embedding techniques to support incremental training. Furthermore, this project focuses on developing novel annotation schemes and new benchmark corpora allowing us to evaluate our mining and alignment methods across topics, text types, and varying timestamps.The outcome will be novel methods for obtaining an Open Argumentation Graph including semantically enriched groups of similar arguments from multiple textual sources linked with support and attack relations. To ensure a wide coverage of argumentation styles, we will apply our methods to different topics frequently discussed in online news and Twitter messages and conduct both component evaluation using annotated gold data and crowd-based post-hoc evaluations.

公开辩论包括如此多的论点，以至于做出明智的决策超出了感兴趣的公众或负责任的专家的认知能力。新的论点是不断贡献的（挑战C1），通常是完整的（C2），并且需要对共同事实或以前的参数进行了解以理解它们（C3）。该项目旨在调查计算方法，以i）不断地提高其能力，以将其与以前的参数相结合，并与以前的参数相结合，并与以前的参数相结合，并与之持续的不完整的参数，并自动逐渐地与自动的参数保持一致，并具有自动化的知识，并将其自动化为自动化的参数，并将其自动化为自动化的论证，并将其自动化为自动化的论证。了解参数。我们通过结合和推进两个研究领域参数挖掘和知识图构建中当前的最新算法来实现这一目标。为了处理正在进行的辩论中的概念漂移，我们旨在通过知识吸引的终身学习方法来推进论证挖掘方法。我们将研究用于学习主题不变论点特征的新型神经体系结构以及参数和辩论主题之间的关系，使用知识图嵌入将语义知识注入神经网络，并利用自我训练来不断扩展训练数据。为了应对不完整的论点，检索到的论点将与已知的论点保持一致，并丰富了背景知识。我们将通过结合链接发现和关键字搜索将参数实体与背景知识联系起来。该链接的背景知识将被整合到增量聚类方法中，以将类似参数分组为参数群集。这些论点群体之间的争论支持和攻击关系将使用监督学习确定。我们的目标是通过结合包含百科全书和常识知识（Babelnet和ConceptNet）的当代语义知识库以及从非结构化的Web Corpora（Common Crawl）中提取知识的知识来自动获取所需的背景知识。为了将这些背景知识集成到机器学习模型中，我们将采用现有的知识嵌入技术来支持增量培训。此外，该项目着重于制定新颖的注释方案和新的基准语料库，使我们能够评估跨主题，文本类型和不同时间戳的采矿和对齐方式。结果将是获得开放论证图的新方法，包括从多个文本源与支持和攻击关系链接的类似论文的开放论证图。为了确保广泛的论证方式覆盖，我们将应用我们的方法应用于在线新闻和Twitter消息中经常讨论的不同主题，并使用带注释的黄金数据和基于人群的事后评估进行两者评估。