RI: Small: Recognizing Implicit Personal States in Natural Language

叙事性文本和个人对话通常围绕着人们发现自己所处的情况展开。目前的自然语言系统只提取事件的字面意义，没有意识到人们是如何受到这些事件的影响的。例如，如果一名男子说他已经被解雇或被诊断出患有癌症，那么系统应该理解他处于不利的情况。如果一名女性说她刚从大学毕业或在工作中获得晋升，那么系统应该理解她处于积极的境地。这个项目开发了一种技术，可以自动识别对人们产生积极或负面影响的情况。情感知识对于情感和社交媒体分析等应用是必不可少的，例如识别经历不利情况的高危个人，这些情况可能使他们对自己或他人构成危险。这项研究开发了自然语言处理技术来识别与事件相关的隐含情感状态。事件和状态被表示为情景框架，并自动从大型文本语料库中获取。使用带有上下文图的半监督标签传播从博客学习隐含情况，以基于主题聚类、事件-事件共现和话语关系传播正面/负面证据。使用自举方法从推文中学习暗示情况，这些方法适用于情感和讽刺的推文。每一种情感情境都会被自动分配一个极性和内涵强度。这项研究推动了人类语言技术的进步，从根本上加深了对叙事文本和对话的情感状态、动机和目标的语言理解。

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.