Natural Language Understanding: Foundations and State-of-the-Art
published: Dec. 5, 2015, recorded: October 2015, views: 6554
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Building systems that can understand human language—being able to answer questions, follow instructions, carry on dialogues—has been a long-standing challenge since the early days of AI. Due to recent advances in machine learning, there is again renewed interest in taking on this formidable task. A major question is how one represents and learns the semantics (meaning) of natural language, to which there are only partial answers. The goal of this tutorial is (i) to describe the linguistic and statistical challenges that any system must address; and (ii) to describe the types of cutting edge approaches and the remaining open problems. Topics include distributional semantics (e.g., word vectors), frame semantics (e.g., semantic role labeling), model-theoretic semantics (e.g., semantic parsing), the role of context, grounding, neural networks, latent variables, and inference. The hope is that this unified presentation will clarify the landscape, and show that this is an exciting time for the machine learning community to engage in the problems in natural language understanding.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !