Ph.D candidate, University of Washington
Email
Abstract
Document-level Sentiment Inference with Social, Faction, and Discourse Context
We present a new approach for document-level sentiment inference, where the goal is to predict directed opinions (\emph{who} feels positively or negatively towards \emph{whom}) for all entities mentioned in a text. To encourage more complete and consistent predictions, we introduce an ILP that jointly models (1) sentence- and discourse-level sentiment cues, (2) factual evidence about entity factions, and (3) global constraints based on social science theories such as homophily, social balance, and reciprocity. Together, these cues allow for rich inference across groups of entities, including for example that CEOs and the companies they lead are likely to have similar sentiment towards others.
We evaluate performance on new, densely labeled data that provides supervision for all pairs, complementing previous work that only labeled pairs mentioned in the same sentence. Experiments demonstrate that the global model outperforms sentence-level baselines, by providing more coherent predictions across sets of related entities.
Bio
Eunsol Choi is a Ph.D student in the Paul G. Allen School of Computer Science and Engineering at the University of Washington, advised by Yejin Choi and Luke Zettlemoyer. Her research focuses on natural language processing, specifically applying machine learning to recover semantics from text. She develops techniques for extracting information about entities from text, and answering natural language questions automatically using large-scale databases or unconstructed text.
In addition to her pursuit at UW, she has research internship experiences at the USC Information Sciences Institute and Google. She has served as a program committee in conferences such as EMNLP, ACL, NAACL and EACL and gave an invited talk at the workshop on question answering at NAACL. Prior to UW, she studied mathematics and computer science at Cornell University and received an honorable mention of the CRA outstanding undergraduate research award.