EAGER: ATAROS: Automatic Tagging and Recognition of Stance

EAGER：ATAROS：自动标记和立场识别

• 批准号: 1351034
• 负责人: Gina-Anne Levow
• 金额: $ 25万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-15 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1351034&HistoricalAwards=false
• 关键词: EAGER; ATAROS; Automatic; Tagging; Recognition

From activities as simple as scheduling a meeting to those as complex as balancing a national budget, people take stances in negotiations and decision making. While the related areas of subjectivity and sentiment analysis have received significant attention, work has focused almost exclusively on text, whereas much stance-taking activity is carried out verbally. Early experiments suggest that people alter their speaking style when engaged in stance-taking, and listeners can much more readily detect negative attitudes by listening to the original speech than by reading transcripts. However, due to the diversity of factors that influence speech production, from individual differences to social context, isolating the signals of stance-taking in speech for automatic recognition presents substantial challenges.This Early Grant for Exploratory Research project represents a focused exploration of spoken interactions to provide a characterization of linguistic factors associated with stance-taking and develop computational methods that exploit these features to automatically detect stance-taking behavior. Robust linguistic markers of stance-taking are identified through analysis of both controlled elicitations and archived recordings of Congressional hearings on the financial crisis. The former allow experimental comparisons to highlight sometimes subtle contrasts, while the latter enable validation and extension of those findings in real-world, high-stakes discussions. The analysis includes novel acoustic-phonetic measures of dynamic patterns in speech, such as vowel space scaling and pitch/energy velocity, with sophisticated visualization techniques developed to support feature exploration. Findings are validated via stance recognition experiments combining acoustic and lexical cues, which lay the foundation for automatic tracking of trends and shifts in attitudes.

从安排会议这样简单的活动到平衡国家预算这样复杂的活动，人们在谈判和决策中都会采取立场。 虽然主观性和情感分析的相关领域受到了极大的关注，但工作几乎完全集中在文本上，而许多立场采取活动都是口头进行的。 早期的实验表明，人们在采取立场时会改变说话风格，而且听众通过聆听原始演讲比阅读文字记录更容易发现消极态度。 然而，由于影响语音产生的因素多种多样，从个体差异到社会背景，隔离语音中的立场采取信号以进行自动识别面临着巨大的挑战。这个早期探索性研究资助项目代表了对口语交互的集中探索，以提供与立场采取相关的语言因素的特征，并开发利用这些特征来自动检测立场采取行为的计算方法。 通过对金融危机国会听证会的受控启发和存档录音的分析，确定了表明立场的强有力的语言标记。 前者允许通过实验比较来突出有时微妙的对比，而后者则可以在现实世界的高风险讨论中验证和扩展这些发现。 该分析包括语音动态模式的新颖声学语音测量，例如元音空间缩放和音调/能量速度，以及为支持特征探索而开发的复杂可视化技术。 研究结果通过结合声音和词汇线索的姿态识别实验得到验证，这为自动跟踪趋势和态度转变奠定了基础。