CUEPAQ: Visual Analytics and Linguistics for Capturing, Understanding, and Explaining Personalized Argument Quality

CUEPAQ:用于捕获、理解和解释个性化论证质量的视觉分析和语言学

基本信息

项目摘要

In our project, Visual Analytics and Linguistics for Capturing, Understanding, and Explaining Personalized Argument Quality (CUEPAQ) we combine methods from the fields of visual analytics and computational linguistic to generate new approaches for the analysis of argument quality in terms of various metrics on different levels of linguistic analysis. Based on this analysis, we provide, so-called, preference profiles, enabling users to gain insights into their personal argumentation behavior, as well as compare it to the behavior of other users.The main goal of this project is to capture, understand, and explain the perceived quality of arguments. To that end, we collect various stylistic, content, and semantic features that influence how arguments are framed and perceived. The central question of this project is how these elements interact to produce arguments that are perceived as high-quality.In answering this question, we contribute to the research on argument quality a visual analytics framework for the rating and ranking of arguments. The system enables rapid analysis of interactions between argument quality and the linguistic expression of an argument. Our framework extracts preference profiles, which capture the annotation behavior of users by indicating the content and stylistic features that particularly affect their rating of arguments. These preference profiles may vary from user to user, or across different user groups. To account for this, we include both expert knowledge on the annotation of argument quality, as well as results of non-expert user ratings in our analysis of argument quality. Based on relative preference comparisons between arguments, the system extracts patterns of linguistic features, both stylistic and interpretational. These features are expected to capture the users' preferences and would thus be reflected in their rating behavior. This externalized knowledge is visualized based on certain guidance strategies and allows both the user and the system to learn from each other. Since the system can keep track of the annotation behavior of different users, this co-adaptive process does not only allow a user to understand their own argumentation preferences but also to compare them with other users of the system, as well as the expert opinions on high-quality argumentation. In this project, we use computational linguistic methods to explore the relationship between linguistic choices and the ranking of arguments by users and systems based on expert-opinion. Concretely, we contribute to the uniform annotation of linguistic features of arguments that are relevant to the judgment of argument quality.
在我们的项目中,可视化分析和语言学捕捉,理解和解释个性化的论点质量(CUEPAQ),我们联合收割机方法从可视化分析和计算语言学领域产生新的方法来分析论点质量的各种指标在不同层次的语言分析。基于这种分析,我们提供了所谓的偏好配置文件,使用户能够深入了解他们的个人论证行为,以及将其与其他用户的行为进行比较。本项目的主要目标是捕捉,理解和解释论证的感知质量。为此,我们收集了各种文体,内容和语义特征,影响如何论点的框架和感知。这个项目的核心问题是这些元素如何相互作用,产生被认为是高质量的论点。在回答这个问题时,我们有助于对论点质量的研究,一个可视化的分析框架,用于对论点进行评级和排名。该系统能够快速分析论证质量和论证的语言表达之间的相互作用。我们的框架提取偏好配置文件,通过指示特别影响他们的参数评级的内容和风格特征来捕获用户的注释行为。这些偏好简档可能因用户而异,或者跨不同的用户组而异。为了解释这一点,我们包括专家知识的注释的论点质量,以及结果的非专家用户评级在我们的分析的论点质量。基于参数之间的相对偏好比较,该系统提取的语言特征,风格和解释模式。这些功能预计将捕捉用户的偏好,从而反映在他们的评级行为。这种外部化的知识是基于某些指导策略可视化的,并允许用户和系统相互学习。由于系统可以跟踪不同用户的注释行为,这种自适应过程不仅允许用户了解自己的论证偏好,而且还可以将其与系统的其他用户以及专家对高质量论证的意见进行比较。在这个项目中,我们使用计算语言学的方法来探索语言选择和用户和系统基于专家意见的参数排名之间的关系。具体地说,我们致力于对与论证质量判断相关的论证语言特征进行统一的诠释。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Professorin Dr. Miriam Butt其他文献

Professorin Dr. Miriam Butt的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Professorin Dr. Miriam Butt', 18)}}的其他基金

Visual Analytics and Linguistics for Interpreting Deliberative Argumentation (VALIDA)
用于解释协商论证的视觉分析和语言学(VALIDA)
  • 批准号:
    376714276
  • 财政年份:
    2017
  • 资助金额:
    --
  • 项目类别:
    Priority Programmes
Information Structure and Questions in Urdu/Hindi
乌尔都语/印地语的信息结构和问题
  • 批准号:
    276392517
  • 财政年份:
    2016
  • 资助金额:
    --
  • 项目类别:
    Research Units
Generating Linguistic Insights in Question Classification throughCombining Explainable Machine Learning and Visualization
通过结合可解释的机器学习和可视化来生成问题分类中的语言见解
  • 批准号:
    276395906
  • 财政年份:
    2016
  • 资助金额:
    --
  • 项目类别:
    Research Units
Coordination Funds
协调基金
  • 批准号:
    276396713
  • 财政年份:
    2016
  • 资助金额:
    --
  • 项目类别:
    Research Units
Visual Analysis of Language Change and Use Patterns
语言变化和使用模式的可视化分析
  • 批准号:
    218458885
  • 财政年份:
    2013
  • 资助金额:
    --
  • 项目类别:
    Research Grants
Computerlinguistische Implementierung einer großen, robusten Grammatik für Urdu/Hindi im Kontext paralleler Grammatikentwicklung
在并行语法开发的背景下,大型、稳健的乌尔都语/印地语语法的计算语言实现
  • 批准号:
    77719491
  • 财政年份:
    2009
  • 资助金额:
    --
  • 项目类别:
    Research Grants

相似国自然基金

基于多幅图象的Visual Hull重构及表面属性建模算法研究
  • 批准号:
    60373031
  • 批准年份:
    2003
  • 资助金额:
    23.0 万元
  • 项目类别:
    面上项目

相似海外基金

CAREER: Promoting Metacognition in Visual Analytics
职业:促进视觉分析中的元认知
  • 批准号:
    2340539
  • 财政年份:
    2024
  • 资助金额:
    --
  • 项目类别:
    Continuing Grant
Cloud-Based Machine Learning and Biomarker Visual Analytics for Salivary Proteomics
基于云的机器学习和唾液蛋白质组生物标志物可视化分析
  • 批准号:
    10827649
  • 财政年份:
    2023
  • 资助金额:
    --
  • 项目类别:
Visual Analytics for Exploration and Hypothesis Generation Using Highly MultiplexedSpatial Data of Tissues and Tumors
使用组织和肿瘤的高度多重空间数据进行探索和假设生成的可视化分析
  • 批准号:
    10743329
  • 财政年份:
    2023
  • 资助金额:
    --
  • 项目类别:
EFRI BRAID: Neuroscience Inspired Visual Analytics
EFRI BRAID:神经科学启发的视觉分析
  • 批准号:
    2318101
  • 财政年份:
    2023
  • 资助金额:
    --
  • 项目类别:
    Standard Grant
Guided Analytics for the Visual Exploration of Higher Dimensional Data
高维数据可视化探索的引导分析
  • 批准号:
    RGPIN-2022-03894
  • 财政年份:
    2022
  • 资助金额:
    --
  • 项目类别:
    Discovery Grants Program - Individual
Visual analytics for time-dependent data
时间相关数据的可视化分析
  • 批准号:
    RGPIN-2018-05508
  • 财政年份:
    2022
  • 资助金额:
    --
  • 项目类别:
    Discovery Grants Program - Individual
Using data augmentation, active learning, and visual analytics for learning with limited examples on mobility data sets
使用数据增强、主动学习和可视化分析,通过移动数据集的有限示例进行学习
  • 批准号:
    DGECR-2022-00386
  • 财政年份:
    2022
  • 资助金额:
    --
  • 项目类别:
    Discovery Launch Supplement
Immersive Visual Analytics for Reservoir Characterization and Analysis
用于油藏表征和分析的沉浸式可视化分析
  • 批准号:
    534866-2019
  • 财政年份:
    2022
  • 资助金额:
    --
  • 项目类别:
    Alexander Graham Bell Canada Graduate Scholarships - Doctoral
Visual Analytics
视觉分析
  • 批准号:
    CRC-2017-00339
  • 财政年份:
    2022
  • 资助金额:
    --
  • 项目类别:
    Canada Research Chairs
Studying Visual Analytics Support for Interactive Information Retrieval within Complex Search Settings
研究复杂搜索设置中交互式信息检索的视觉分析支持
  • 批准号:
    RGPIN-2017-06446
  • 财政年份:
    2022
  • 资助金额:
    --
  • 项目类别:
    Discovery Grants Program - Individual
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了