Events Calendar

CS Colloquium: Sameer Singh (University of Washington) - Interactive Machine Learning for Information Extraction

Speaker: Sameer Singh, University of Washington

Talk Title: Interactive Machine Learning for Information Extraction

Series: CS Colloquium

Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium

Most of the world's knowledge, be it factual news, scholarly research, social communication, subjective opinions, or even fictional content, is now easily accessible as digitized text. Unfortunately, due to the unstructured nature of text, much of the useful content in these documents is hidden. The goal of "information extraction" is to address this problem: extracting meaningful, structured knowledge (such as graphs and databases) from text collections. The biggest challenges when using machine learning for information extraction include the high cost of obtaining annotated data and lack of guidance on how to fix mistakes.

In this talk, I propose interpretable representations that allow users and machine learning models to interact with each other: enabling users to inject domain knowledge into machine learning, and providing explanations for why a machine learning model is making a specific prediction. I study these techniques using relation extraction as the application, an important subtask of information extraction where the goal is to identify the types of relations between entities that are expressed in text. I first describe how symbolic domain knowledge, if provided by the user as first-order logic statements, can be injected into relational embeddings to improve the predictions. In the second part of the talk, I present an approach to "explain" any machine learning prediction using a symbolic representation, which the user may annotate directly for more effective supervision. I present experiments that demonstrate that an interactive interface between a user and machine learning is effective in reducing annotation effort and in quickly training accurate extraction systems.

Biography: Sameer Singh is a Postdoctoral Research Associate at the University of Washington, working on large-scale and interactive machine learning applied to information extraction and natural language processing. He received his PhD from the University of Massachusetts, Amherst, during which he also interned at Microsoft Research, Google Research, and Yahoo! Labs on massive-scale machine learning. He was recently selected as a DARPA Riser, won the grand prize in the Yelp dataset challenge, has been awarded the Yahoo! Key Scientific Challenges and the UMass Graduate School fellowships, and was a finalist for the Facebook PhD fellowship.

Host: CS Department

Location: Olin Hall of Engineering (OHE) - 136

Audiences: Everyone Is Invited

Contact: Assistant to CS chair