OVERVIEW

1. Existing algorithms for FINE-GRAINED OPINION EXTRACTION can to some extent identify and characterize private states in text when they are expressed explicitly. Similarly, existing algorithms for SEMANTIC EQUIVALENCE and NOVELTY DETECTION have focused to date on facts and event data. OVERVIEW HIGH-LEVEL SYSTEM ARCHITECTURE CURRENT STATE OF THE ART Our existing systems can: Recognize and extract explicit private states in newswire • Joint opinion+source recognition: 70F • Opinion expressions: • 72F (direct expressions) The congressman criticizedObamacare. • 66F (indirect expressions) They ignored the unreasonable customer. • Contextual polarity given polar word: • 78% accuracy Identify semantic relatedness between texts using lexical matching approaches • Senseval2012: 0.61 Pearson EXPECTED IMPACT Claire Cardie (Cornell) RadaMihalcea (UNT) JanyceWiebe (Pittsburgh) Uncovering Motivations, Stances and Anomalies Through Private-State Recognition and Interpretation BloggerX: The international community seems to be tolerating the Israeli campaign against the Palestinians. Cross-Document Tracking of Private States Private-State Extraction Private-State Database • WE PROPOSE TO • Identify the rich spectrum of private states • expressed not explicitly but through implicature(i.e. inference) and connotation; • Track private states through discourse and across documents; and • Produce systems for private-state-awaresemantic equivalence and novelty detection. [explicit] Int’l community:  Israeli campaign [inferred] BloggerX:  Israeli campaign  Israel Palestinians • BloggerX: • Palestinians •  Israel • Turkey • … … Private-state-aware Semantic Equivalence and Novelty Detection EXAMPLES The people are happy because Chavez has fallen. [explicit] The people:  Chavez falling [inferred] The people:Chavez himself BloggerY: It is no surprise then that MoveOn would attack Senator McCain [explicit] MoveOnSenator McCain [inferred] BloggerY • Our private-state-aware semantic equivalence and novelty detection algorithms will assist analysts in: • Recognizing shared beliefs among key participants; • Determining changes in the attitudes and beliefs of key participants; • Detecting contradictions among expressed and inferred opinions, emotions, and attitudes; and • Identifying emerging or disintegrating alliances. *attitude change*: BloggerX Palestinians • Cross-Document Private-State Tracking • Within-document and cross-document coreference resolution • Private-state recognition in conversational data • Discourse-level integration of explicit private states, connotations, and inferred private states • Private-State-Aware Semantic Equivalence and Novelty Detection • Private-state aware semantic relatedness • Sentence-level novelty detection • Novelty detection on protagonist-centered event graphs • Private-State Extraction • Representation and acquisition of connotation lexical knowledge • Improved recognition of explicit private states • Compositional calculation of polarity • Novel framework for representing and processing private-state implicature  ? MO MO OR Evidence from throughout the discourse must be marshaled to choose which set of inferences is more probable …. ….  …  … MO attacking SM MO attacking SM   Contact information: cardie@cs.cornell.edu rada@cs.unt.edu wiebe@cs.pitt.edu  MoveOn  MoveOn  Senator McCain Senator McCain 