1. RESEARCH POSTER PRESENTATION DESIGN © 2012 www.PosterPresentations.com © 2012 PosterPresentations.com 2117 Fourth Street, Unit C Berkeley CA 94710 posterpresenter@gmail.com Student discounts are available on our Facebook page. Go to PosterPresentations.com and click on the FB icon. 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 criticized Obamacare. 66F (indirect expressions) They ignored the unreasonable customer. Contextual polarity given polar word: 78% accuracy Identify semantic relatedness between texts using lexical matching approaches Senseval 2012: 0.61 Pearson EXPECTED IMPACT Claire Cardie (Cornell)Rada Mihalcea (UNT)Janyce Wiebe (Pittsburgh) Uncovering Motivations, Stances and Anomalies Through Private-State Recognition and Interpretation 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-aware semantic equivalence and novelty detection. Private-State Extraction Cross-Document Tracking of Private States Private-state-aware Semantic Equivalence and Novelty Detection BloggerX: The international community seems to be tolerating the Israeli campaign against the Palestinians. BloggerX:  Palestinians  Israel  Turkey … [explicit] Int’l community:  Israeli campaign [inferred] BloggerX:  Israeli campaign  Israel  Palestinians … *attitude change*: BloggerX  Palestinians Private-State Database 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. 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 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 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    MO attacking SM MoveOn Senator McCain    MO attacking SM MoveOn Senator McCain OR Evidence from throughout the discourse must be marshaled to choose which set of inferences is more probable  ….  …  MO  ….  …  MO  ? EXAMPLES Contact information: cardie@cs.cornell.edu rada@cs.unt.edu wiebe@cs.pitt.edu