Structured Prediction for Natural Language Processing
published: Sept. 17, 2009, recorded: June 2009, views: 1320
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.
This tutorial will discuss the use of structured prediction methods from machine learning in natural language processing. The field of NLP has, in the past two decades, come to simultaneously rely on and challenge the field of machine learning. Statistical methods now dominate NLP, and have moved the field forward substantially, opening up new possibilities for the exploitation of data in developing NLP components and applications. However, formulations of NLP problems are often simplified for computational or practical convenience, at the expense of system performance. This tutorial aims to introduce several structured prediction problems from NLP, current solutions, and challenges that lie ahead. Applications in NLP are a mainstay at ICML conferences; many ML researchers view NLP as a primary or secondary application area of interest. This tutorial will help the broader ML community understand this important application area, how progress is measured, and the trade-offs that make it a challenge.
Download slides: icml09_smith_spn_01.pdf (11.7 MB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !