Exploiting Relational Knowledge For Extraction Of Opinions And Events In Text
The richness and diversity of natural language makes automatic extraction of opinions and events from texts difficult. An automatic system designed for this task would need to identify complex linguistic expressions, interpret their meanings in context, and integrate information that is often distributed over long distances. While machine learning techniques have been widely applied for information extraction, they often make strong independence assumptions about linguistic structure and make decisions myopically based on local and partial information in the text. In this talk, I argue that accurate information extraction needs machine learning algorithms that can exploit relationships within and across multiple levels --- between words, phrases and sentences --- facilitating globally-informed decisions.
In the first part of my talk, I will introduce the task of fine-grained opinion extraction --- discovering opinions, their sources, targets and sentiment from text. I will present a joint inference approach that can account for the dependencies among different opinion entities and relations, and a context-aware learning approach that is capable of exploiting intra- and inter-sentential discourse relations for improving sentiment prediction. In the second part of my talk, I will present my recent work on event extraction and event coreference resolution --- the task of extracting event mentions and integrating them within and across documents by exploiting context. I propose a novel Bayesian model that allows generative modeling of event mentions, while simultaneously accounting for event-specific similarity.
Bishan Yang is a Ph.D. candidate at Cornell University. Her research interests lie in natural language processing and machine learning. She earned her M.S. and B.S. in Computer Science from Peking University. During her graduate studies, she has completed internships at Microsoft Research, Google Research and eBay Research Labs.