RI: Small: Recognizing Implicit Personal States in Natural Language

叙事文本和个人对话通常围绕着人们发现自己所处的情况。目前的自然语言系统只提取事件的字面意义，无法识别人们如何受到它们的影响。 例如，如果一个人说他被解雇了或被诊断出患有癌症，那么系统应该理解他处于消极状态。如果一个女人说她刚从大学毕业，或者在工作中得到了提升，那么系统应该理解她处于积极的状态。该项目开发的技术可以自动识别对人们产生积极或消极影响的情况。情感知识对于情感和社交媒体分析等应用至关重要，例如识别经历不利情况的高危个体，这些情况可能使他们对自己或他人构成危险。本研究开发了自然语言处理技术，以识别与事件相关的隐式情感状态。 事件和状态表示为情景框架，并自动从大型文本语料库中获取。 使用半监督标签传播与上下文图传播的主题聚类，事件-事件共现，和话语关系的基础上的积极/消极的证据，从博客中学习到的情况。使用应用于情感和讽刺推文的自举方法，从推文中学习到隐含的情况。每一种情感情境都被自动地赋予了一种极性和内涵强度。这项研究推动人类语言技术从根本上更深入地理解叙事文本和会话对话的情感状态，动机和目标。

Narrative texts and personal conversations typically revolve around situations that people find themselves in. Current natural language systems extract only the literal meaning of events, failing to recognize how people are impacted by them. For example, if a man says that he has been laid off or diagnosed with cancer, then a system should understand that he is in a negative situation. If a woman says that she just graduated from college or has been promoted at work, then a system should understand that she is in a positive situation. This project develops technology to automatically identify situations that positively or negatively impact people. Affective knowledge is essential for applications such as sentiment and social media analysis, for example to recognize at-risk individuals experiencing adverse situations that may make them a danger to themselves or others.This research develops natural language processing technology to recognize implicit affective states associated with events. Events and states are represented as situation frames and automatically harvested from large text corpora. Implicating situations are learned from blogs using semi-supervised label propagation with context graphs to propagate positive/negative evidence based on topic clustering, event-event co-occurrence, and discourse relations. Implicating situations are learned from tweets using bootstrapping methods applied to affective and sarcastic tweets. Each affective situation is automatically assigned a polarity and connotative strength. This research advances human language technology toward fundamentally deeper language understanding about affective states, motivations, and goals for narrative text and conversational dialogue.