Abstract: Text can be a great source of market signals. In the past, most text analytics for market signal extraction was fairly simple, word count based. Now, as finance firms build data science groups, the complexity and scale of the available text analytics is much greater. However, the basic central problems remain - acquire text data, clean text data, link text data to market-relevant entities and events. In this talk, I will showcase some applications of text analytics in finance, and some differences between text analytics applications in the financial industry and "standard" NLP.
Bio: Amanda Stent is a NLP architect at Bloomberg. Previously, she was a director of research and principal research scientist at Yahoo Labs, a principal member of technical staff at AT&T Labs - Research, and an associate professor in the Computer Science Department at Stony Brook University. Her research interests center on natural language processing and its applications, in particular topics related to text analytics, discourse, dialog and natural language generation. She holds a PhD in computer science from the University of Rochester. She is co-editor of the book Natural Language Generation in Interactive Systems (Cambridge University Press), has authored over 90 papers on natural language processing and is co-inventor on over twenty patents and patent applications. She is president emeritus of the ACL/ISCA Special Interest Group on Discourse and Dialog, treasurer of the ACL Special Interest Group on Natural Language Generation and one of the rotating editors of the journal Dialogue & Discourse. She is also a board member of CRA-W, where she co-edits the newsletter.